Dissertations / Theses on the topic 'Heuristic'
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1
Peake, Katharine Louise. "Composition heuristics and theories and a proposed heuristic for business writing." CSUSB ScholarWorks, 2007. https://scholarworks.lib.csusb.edu/etd-project/3282.
2
Ambrogi, Timothy. "Heuristic counterpoint." Diss., Connect to the thesis, 2004. http://hdl.handle.net/10066/1484.
3
Murthy, Sapna Guniguntla. "Disaster recovery heuristic : a mapping heuristic for optimum retrieval /." Online version of thesis, 2009. http://hdl.handle.net/1850/10733.
4
GUTIERRES, RICARDO. "ADAPTIVE HEURISTIC CONTROLLERS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1991. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9409@1.
Abstract:
CENTRO DE PESQUISAS LEOPOLDO AMÉRICO MIGUEZ DE MELLOUm controlador Heurístico Adaptativo baseia-se num conjunto de regras lingüísticas para conduzir um processo com modelo impreciso ou complexo ao estado desejado. O comportamento do processo deve respeitar os requisitos de performance predefinidos. Para satisfazer estes objetivos, a estrutura interna do controle sofre mudanças para adequá- la as condições vigentes no processo. Os métodos de adaptação abordados consideram a modificação de uma estrutura matricial interpretada como as correções incrementais, compatíveis com os ajustes a serem efetuados sobre o processo, ou como regras, constituídas por variáveis nebulosas, que requerem manipulações adicionais para produzir a saída do controlador. Em qualquer dos casos, a adaptação é realizada a partir de uma Tabela de Índices de Performance. Para facilitar a sua obtenção é implementado um procedimento, que fornece a representação matricial das regras lingüísticas, concatenadas na forma de um Algoritmo Lingüístico de Controle. O comportamento dinâmico do Sistema, composto pelos Controladores Heurísticos e por processos com modelos distintos, é considerado para Tabelas de índices de Performance com várias dimensões. As regras lingüísticas, correlacionadas com estas tabelas, foram elaboradas com diversas classes de atributos. As simulações realizadas concentram-se sobre os parâmetros dos controladores, que influenciam significativa- Os estudos abordam também o comportamento da estrutura interna destes controladores e o seu desempenho em termos da velocidade de atuação sobre o processo.
A heuristic Controller uses a set of linguistic rules, which are derived from expertise or human operators´ skills, in order to achieve control of processes that have inaccurate or complex models. An adaptative Heuristic Controller adjusts the set of rules in an automatic and continuous way, aiming to achieve prescribed objectives indicated by a performance measure. The adaptative procedures modify a matrix, the elements of which are either incremental corrections or numeric rules associated with fuzzy variables. In both cases a Performance Index Table and a learning method are employed to correct that matrix. The Performance Table is a matrix calculated from a set of linguistic rules. The controllers are implemented with different Performance Tables, considering various sets of linguistic values and quantization levels. The dynamic behaviour of overdamped and underdamped processes is investigated. The performance of simulated systems is analyzed with respect to relevant parameters that affect their behaviour.
5
Perry, Kristine. "Heuristic weighted voting /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2120.pdf.
6
Monteith, Kristine Perry. "Heuristic Weighted Voting." BYU ScholarsArchive, 2007. https://scholarsarchive.byu.edu/etd/1206.
Abstract:
Selecting an effective method for combining the votes of classifiers in an ensemble can have a significant impact on the overall classification accuracy an ensemble is able to achieve. With some methods, the ensemble cannot even achieve as high a classification accuracy as the most accurate individual classifying component. To address this issue, we present the strategy of Heuristic Weighted Voting, a technique that uses heuristics to determine the confidence that a classifier has in its predictions on an instance by instance basis. Using these heuristics to weight the votes in an ensemble results in an overall average increase in classification accuracy over when compared to the most accurate classifier in the ensemble. When considering performance over 18 data sets, Heuristic Weighted Voting compares favorably both in terms of average classification accuracy and algorithm-by-algorithm comparisons in accuracy when evaluated against three baseline ensemble creation strategies as well as the methods of stacking and arbitration.
7
Silva, Renato Teixeira da [UNESP]. "Aplicação de meta-heurísticas na resolução do problema de balanceamento e designação de trabalhadores com deficiência em linha de produção." Universidade Estadual Paulista (UNESP), 2012. http://hdl.handle.net/11449/93081.
Abstract:
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silva_rt_me_guara.pdf: 445223 bytes, checksum: f6563e16194940a8f4f8abc7c03ac033 (MD5)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
A Organização Internacional do Trabalho estima que existem cerca de 650 milhões de pessoas com deficiência em idade produtiva. No entanto, esta parcela da população possui altos índices de desemprego devido a várias barreiras. Uma alternativa para facilitar a inclusão dessas pessoas é a criação de Centros de Trabalho para pessoas com Deficiência (CTD`s) onde as pessoas com deficiência tenham a oportunidade de experimentar um ambiente de trabalho real antes de irem para um emprego “normal”. Neste tipo de ambiente, onde é impossível ao gestor prever quais trabalhadores estarão disponíveis a cada dia devido às altas taxas de absenteísmo, há a necessidade de se definir uma organização mais produtiva diariamente. Neste contexto se torna oportuna a utilização do Problema de Balanceamento de Linha e Designação de Trabalhadores (em inglês ALWABP), onde se busca minimizar o tempo de ciclo a partir de um dado número de trabalhadores, alocando tarefas às estações de trabalho e trabalhadores às estações, tendo em vista que alguns trabalhadores podem ser muito lentos para executar certas tarefas ou até incapazes, devido a alguma deficiência que eles apresentam, e muito eficientes na execução de outras. O objetivo geral desta dissertação consiste em empregar diferentes meta-heurísticas para resolver o ALWABP, comparando com os melhores resultados das instâncias encontradas na literatura. Dentre várias meta-heurísticas disponíveis na literatura foram utilizados o Harmony Search (HS), o Adaptive Large Neighborhood Search (ALNS) e o Clustering Search (CS) utilizando o HS e o ALNS como heurísticas geradoras de soluções. Cada uma das quatro implementações foram testadas em 320 instâncias propostas na literatura divididas em quatro famílias. Os experimentos computacionais mostraram bons resultados...
The International Labour Organization estimates that there are approximately 650 million disabled people in working age. However, this population presents high rates of unemployment due to numerous barriers. An alternative to facilitate the inclusion of these people is the establishment of Centers for Working People with Disabilities where people with disabilities have the opportunity to experience a real work environment before going to a “normal” job. In this type of environment, where it is impossible to predict which workers will be available each day due to high rates of absence in this population, there is a need to define a more productive organization on a daily basis. In this context it becomes appropriate to use the Assembly Line Worker Assignment and Balancing Problem (ALWABP), which seeks to minimize the cycle time for a given number of workers, assigning tasks to workstations and workers to stations, considering that some workers may be too slow to perform certain tasks, or even unable due to some deficiency they present, and very efficient in performing others. The aim of this dissertation is to employ different meta-heuristics to solve the ALWABP, comparing with the best results of instances found in the literature. Among several meta-heuristics available in the literature were used Harmony Search (HS), Adaptive Large Neighborhood Search (ALNS) and Clustering Search (CS) using the HS and ALNS as heuristics for the generation of solutions. Each of the four implementations has been tested in 320 instances proposed in the literature, classified into four families. The computational experiments showed good results, and in some instances obtaining better solution values best known. Conclusions regarding... (Complete abstract click electronic access below)
8
Silva, Renato Teixeira da. "Aplicação de meta-heurísticas na resolução do problema de balanceamento e designação de trabalhadores com deficiência em linha de produção /." Guaratinguetá : [s.n.], 2012. http://hdl.handle.net/11449/93081.
Abstract:
Orientador: Galeno José de SenaBanca: Marcos Antonio Pereira
Banca: Anibal Tavares de Azevedo
Resumo: A Organização Internacional do Trabalho estima que existem cerca de 650 milhões de pessoas com deficiência em idade produtiva. No entanto, esta parcela da população possui altos índices de desemprego devido a várias barreiras. Uma alternativa para facilitar a inclusão dessas pessoas é a criação de Centros de Trabalho para pessoas com Deficiência (CTD's) onde as pessoas com deficiência tenham a oportunidade de experimentar um ambiente de trabalho real antes de irem para um emprego "normal". Neste tipo de ambiente, onde é impossível ao gestor prever quais trabalhadores estarão disponíveis a cada dia devido às altas taxas de absenteísmo, há a necessidade de se definir uma organização mais produtiva diariamente. Neste contexto se torna oportuna a utilização do Problema de Balanceamento de Linha e Designação de Trabalhadores (em inglês ALWABP), onde se busca minimizar o tempo de ciclo a partir de um dado número de trabalhadores, alocando tarefas às estações de trabalho e trabalhadores às estações, tendo em vista que alguns trabalhadores podem ser muito lentos para executar certas tarefas ou até incapazes, devido a alguma deficiência que eles apresentam, e muito eficientes na execução de outras. O objetivo geral desta dissertação consiste em empregar diferentes meta-heurísticas para resolver o ALWABP, comparando com os melhores resultados das instâncias encontradas na literatura. Dentre várias meta-heurísticas disponíveis na literatura foram utilizados o Harmony Search (HS), o Adaptive Large Neighborhood Search (ALNS) e o Clustering Search (CS) utilizando o HS e o ALNS como heurísticas geradoras de soluções. Cada uma das quatro implementações foram testadas em 320 instâncias propostas na literatura divididas em quatro famílias. Os experimentos computacionais mostraram bons resultados... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: The International Labour Organization estimates that there are approximately 650 million disabled people in working age. However, this population presents high rates of unemployment due to numerous barriers. An alternative to facilitate the inclusion of these people is the establishment of Centers for Working People with Disabilities where people with disabilities have the opportunity to experience a real work environment before going to a "normal" job. In this type of environment, where it is impossible to predict which workers will be available each day due to high rates of absence in this population, there is a need to define a more productive organization on a daily basis. In this context it becomes appropriate to use the Assembly Line Worker Assignment and Balancing Problem (ALWABP), which seeks to minimize the cycle time for a given number of workers, assigning tasks to workstations and workers to stations, considering that some workers may be too slow to perform certain tasks, or even unable due to some deficiency they present, and very efficient in performing others. The aim of this dissertation is to employ different meta-heuristics to solve the ALWABP, comparing with the best results of instances found in the literature. Among several meta-heuristics available in the literature were used Harmony Search (HS), Adaptive Large Neighborhood Search (ALNS) and Clustering Search (CS) using the HS and ALNS as heuristics for the generation of solutions. Each of the four implementations has been tested in 320 instances proposed in the literature, classified into four families. The computational experiments showed good results, and in some instances obtaining better solution values best known. Conclusions regarding... (Complete abstract click electronic access below)
Mestre
9
Hosny, Manar Ibrahim. "Investigating heuristic and meta-heuristic algorithms for solving pickup and delivery problems." Thesis, Cardiff University, 2010. http://orca.cf.ac.uk/55181/.
Abstract:
The development of effective decision support tools that can be adopted in the transportation industry is vital in the world we live in today, since it can lead to substantial cost reduction and efficient resource consumption. Solving the Vehicle Routing Problem (VRP) and its related variants is at the heart of scientific research for optimizing logistic planning. One important variant of the VRP is the Pickup and Delivery Problem (PDP). In the PDP, it is generally required to find one or more minimum cost routes to serve a number of customers, where two types of services may be performed at a customer location, a pickup or a delivery. Applications of the PDP are frequently encountered in every day transportation and logistic services, and the problem is likely to assume even greater prominence in the future, due to the increase in e-commerce and Internet shopping. In this research we considered two particular variants of the PDP, the Pickup and Delivery Problem with Time Windows (PDPTW), and the One-commodity Pickup and Delivery Problem (1-PDP). In both problems, the total transportation cost should be minimized, without violating a number of pre-specified problem constraints. In our research, we investigate heuristic and meta-heuristic approaches for solving the selected PDP variants. Unlike previous research in this area, though, we try to focus on handling the difficult problem constraints in a simple and effective way, without complicating the overall solution methodology. Two main aspects of the solution algorithm are directed to achieve this goal, the solution representation and the neighbourhood moves. Based on this perception, we tailored a number of heuristic and meta-heuristic algorithms for solving our problems. Among these algorithms are: Genetic Algorithms, Simulated Annealing, Hill Climbing and Variable Neighbourhood Search. In general, the findings of the research indicate the success of our approach in handling the difficult problem constraints and devising simple and robust solution mechanisms that can be integrated with vehicle routing optimization tools and used in a variety of real world applications
10
Sanusi, Afeez Ayinla. "Train Dispatching: Heuristic Optimization." Thesis, Högskolan Dalarna, Datateknik, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:du-4107.
Abstract:
Train dispatchers faces lots of challenges due to conflicts which causes delays of trains as a result of solving possible dispatching problems the network faces. The major challenge is for the train dispatchers to make the right decision and have reliable, cost effective and much more faster approaches needed to solve dispatching problems. This thesis work provides detail information on the implementation of different heuristic algorithms for train dispatchers in solving train dispatching problems. The library data files used are in xml file format and deals with both single and double tracks between main stations. The main objective of this work is to build different heuristic algorithms to solve unexpected delays faced by train dispatchers and to help in making right decisions on steps to take to have reliable and cost effective solution to the problems. These heuristics algorithms proposed were able to help dispatchers in making right decisions when solving train dispatching problems.
11
Aslan, Burak Galip Püskülcü Halis. "Heuristic container placement algorithms/." [s.l.]: [s.n.], 2003. http://library.iyte.edu.tr/tezler/master/bilgisayaryazilimi/T000268.rar.
12
Henry, Obit Joe. "Developing novel meta-heuristic, hyper-heuristic and cooperative search for course timetabling problems." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/13581/.
Abstract:
The research presented in this PhD thesis focuses on the problem of university course timetabling, and examines the various ways in which metaheuristics, hyperheuristics and cooperative heuristic search techniques might be applied to this sort of problem. The university course timetabling problem is an NP-hard and also highly constrained combinatorial problem. Various techniques have been developed in the literature to tackle this problem. The research work presented in this thesis approaches this problem in two stages. For the first stage, the construction of initial solutions or timetables, we propose four hybrid heuristics that combine graph colouring techniques with a well-known local search method, tabu search, to generate initial feasible solutions. Then, in the second stage of the solution process, we explore different methods to improve upon the initial solutions. We investigate techniques such as single-solution metaheuristics, evolutionary algorithms, hyper-heuristics with reinforcement learning, cooperative low-level heuristics and cooperative hyper-heuristics. In the experiments throughout this thesis, we mainly use a popular set of benchmark instances of the university course timetabling problem, proposed by Socha et al. [152], to assess the performance of the methods proposed in this thesis. Then, this research work proposes algorithms for each of the two stages, construction of initial solutions and solution improvement, and analyses the proposed methods in detail. For the first stage, we examine the performance of the hybrid heuristics on constructing feasible solutions. In our analysis of these algorithms we discovered that these hybrid approaches are capable of generating good quality feasible solutions in reasonable computation time for the 11 benchmark instances of Socha et al. [152]. Just for this first stage, we conducted a second set of experiments, testing the proposed hybrid heuristics on another set of benchmark instances corresponding to the international timetabling competition 2002 [91J]. Our hybrid construction heuristics were also capable of producing feasible solutions for the 20 instances of the competition in reasonable computation time. It should be noted however, that most of the research presented here was focused on the 11 problem instances of Socha et al. [152]. For the second stage, we propose new metaheuristic algorithms and cooperative hyper-heuristics, namely a non-linear great deluge algorithm, an evolutionary nonlinear great deluge algorithm (with a number of new specialised evolutionary operators), a hyper-heuristic with a learning mechanism approach, an asynchronous cooperative low-level heuristic and an asynchronous cooperative hyper-heuristic. These two last algorithms were inspired by the particle swarm optimisation technique. Detailed analyses of the proposed algorithms are presented and their relative benefits discussed. Finally, we give our suggestions as to how our best performing algorithms might be modified in order to deal with a wide range of problem domains including more real-world constraints. We also discuss the drawbacks of our algorithms in the final section of this thesis.
13
Johnsson, Pontus. "Riskbedömning och beslutsfattande vid bränder : En utvärdering av verkliga scenarion utifrån ett heuristiskt perspektiv." Thesis, Linköping University, Department of Computer and Information Science, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-57152.
Abstract:
I syfte att förbättra kunskapsläget kring människors beteenden vid bränder och utrymningar studerades fyra brandsituationer hämtade från ett flertal verkliga händelser ur ett beslutsfattande- och riskbedömningsperspektiv. Det teoretiska underlaget hämtades ur Kahnemans och Tverskys forskning kring heuristiker (Kahneman och Tversky, 1974; Kahneman, Slovic & Tversky, 1982; Gilovich, Griffin & Kahneman, 2002). För ändamålet användes tre heuristiska regler: tillgänglighet, representativitet och affekt. Dessa tre heuristiker möjliggör ögonblickssnabba riskbedömningar genom att allt utom en särskild variabel bortses från i beslutsprocessen. När människor blir stressade tenderar de att förlita sig mer på heuristiker i sina bedömningar. Analysen visar att det är rimligt att anta att de beteenden som observerats i samband med bränder i de fyra fallen beror på beslut huvudsakligen fattade med hjälp av någon av de tre heuristikerna. Denna kunskap kan öppna upp nya möjligheter för att förebygga dödsfall på grund av felaktiga beteenden i samband med bränder och utrymningar.
14
Furcy, David Andre. "Speeding Up the Convergence of Online Heuristic Search and Scaling Up Offline Heuristic Search." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/4855.
Abstract:
The most popular methods for solving the shortest-path problem in
Artificial Intelligence are heuristic search algorithms. The main
contributions of this research are new heuristic search algorithms
that are either faster or scale up to larger problems than existing
algorithms. Our contributions apply to both online and offline tasks.
For online tasks, existing real-time heuristic search algorithms learn
better informed heuristic values and in some cases eventually converge
to a shortest path by repeatedly executing the action leading to a
successor state with a minimum cost-to-goal estimate. In contrast, we
claim that real-time heuristic search converges faster to a shortest
path when it always selects an action leading to a state with a
minimum f-value, where the f-value of a state is an estimate of the
cost of a shortest path from start to goal via the state, just like in
the offline A* search algorithm. We support this claim by implementing
this new non-trivial action-selection rule in FALCONS and by showing
empirically that FALCONS significantly reduces the number of actions
to convergence of a state-of-the-art real-time search algorithm.
For offline tasks, we improve on two existing ways of scaling up
best-first search to larger problems. First, it is known that the WA*
algorithm (a greedy variant of A*) solves larger problems when it is
either diversified (i.e., when it performs expansions in parallel) or
committed (i.e., when it chooses the state to expand next among a
fixed-size subset of the set of generated but unexpanded states). We
claim that WA* solves even larger problems when it is enhanced with
both diversity and commitment. We support this claim with our MSC-KWA*
algorithm. Second, it is known that breadth-first search solves
larger problems when it prunes unpromising states, resulting in the
beam search algorithm. We claim that beam search quickly solves even
larger problems when it is enhanced with backtracking based on limited
discrepancy search. We support this claim with our BULB algorithm. We
show that both MSC-KWA* and BULB scale up to larger problems than
several state-of-the-art offline search algorithms in three standard
benchmark domains. Finally, we present an anytime variant of BULB and
apply it to the multiple sequence alignment problem in biology.
15
Jochumsson, Thorvaldur. "Heuristic multi-sequence search methods." Thesis, University of Skövde, Department of Computer Science, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-530.
Abstract:
With increasing size of sequence databases heuristic search approaches have become necessary. Hidden Markov models are the best performing search methods known today with respect to discriminative power, but are too time complex to be practical when searching in large sequence databases. In this report, heuristic algorithms that reduce the search space before searching with traditional search algorithms of hidden Markov models are presented and experimentally validated. The results of the validation show that the heuristic search algorithms will speed up the searches without decreasing their discriminative power.
16
Choi, Hyun. "TOT: the association strength heuristic." Texas A&M University, 2005. http://hdl.handle.net/1969.1/2629.
Abstract:
Three experiments were conducted to examine the effect of association strength on TOT (tip-of-the-tongue states) and recall. Two hundred nineteen undergraduate students studied pictures and names of 24 imaginary animals that were presented on a large computer screen. The strength of association between the cue and target was manipulated by varying the number of times the picture and the name were presented simultaneously, while keeping the number of presentations for each picture or the target constant across conditions. After the study phase, participants were cued by each picture to recall the imaginary animal names. Participants were asked to rate their strength of TOT on a scale ranging 0 to 3 for each item if they could not think of the name at the moment. Participants also made subjective judgments as to how many times they saw the picture and name of the animal co-occur on the same screen at the study phase, and then they performed a recognition test at the end. The results indicated that the frequency and strength of TOTs linearly increased as a function of number of co-occurrences; the correlation between TOT strength and the participants??
subjective estimation of number of co-occurrences was greater than the correlation between TOT strength and the actual number of co-occurrences. This pattern of results was found even when recall increased along with the increase in number of co-occurrences and was more pronounced particularly when recall was reduced either by interference (Experiment 1) or by increased number of critical items (Experiments 2 & 3) and also by a reduced number of co-occurrence conditions and an increased gap between one level to the next (Experiment 3). Results suggest that an increase in association strength concomitantly increases TOT strength especially when the activation of the target is under threshold for recall and that people may use rules of thumb, or heuristic when they report TOTs by estimating the strength of the cue-target association.
17
Yahaya, Abubakar. "Heuristic approaches to portfolio optimization." Thesis, Lancaster University, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.587501.
Abstract:
One of the most frequently studied areas in finance is the classical mean-variance portfolio selection model pioneered by Harry Markowitz; which is also, undoubtedly recognized as the foundation of modem portfolio theory. The model in its basic form deals with the selection of portfolio of assets such that a reasonable trade-off is achieved between the conflicting objectives. of maximum possible return at a minimum risk, given that the right choice of constituent assets is made and proper weights are allocated. However, despite its enormous contribution to this branch of knowledge, the model is not immune from criticisms ranging from those associated with its in ability to capture the realism of an investment setting - such as transaction costs, cardinality constraints, floor and ceiling constraints, etc. In this research we extended the classical model by incorporating into it the cardinality as well as the floor & ceiling constraints after which we implemented six different metaheuristic algorithms to solve this advanced model. We then designed and implemented some neighbourhood transition strategies to enable our designed algorithms solve the problem in an efficient and intelligent way. Furthermore, we proposed a new portfolio selection model with target-semivariance (as defined in a previous research) as the objective, and constrained by additional real life (cardinality and floor & ceiling) constraints.
18
Eldershaw, Craig. "Heuristic algorithms for motion planning." Thesis, University of Oxford, 2001. http://ora.ox.ac.uk/objects/uuid:ba8e63e9-58ae-4c7e-837f-08e471e858fb.
Abstract:
Motion planning is an increasingly important field of research. Factory automation is becoming more prevalent and at the same time, production runs are shortening in the name of customisation. With computer controlled equipment becoming cheaper and more modular, setting up near-fully automated production lines is becoming fast and easy. This means that the actual programming of the robots and assembly system is becoming the rate determining step. Automated motion planning is a possible solution to this—but only if it can run fast enough. Many heuristic planners have been created in an attempt to achieve the necessary speeds in off-line (or more ambitiously, on-line) processing. This thesis aims to show that different types of heuristic planners can be designed to take advantage of specialised environments or robot characteristics. To show this, three distinct classes of heuristic planners are put forward for discussion. The first of these classes, addressed in Chapter 2, is of very generic planners which will work in virtually all situations (ie. almost any combination of robot and environment). This generality is obviously useful when lacking more specific domain knowledge. However these methods do suffer performance-wise in comparison with more specialised planners when there are characteristics of the problem which can be targeted. Chapter 3 moves to planners which are designed to specifically address certain peculiarities of the environment. Particular focus is given to environments whose corresponding configuration-spaces contain narrow gaps and passages. Finally Chapter 4 addresses a third class of planners: those which are designed for specific types of robots and movements. The particular focus is on locomotion for legged vehicles. For each of these three classes, some discussion is made of existing planners which can be so characterised. In addition, a novel algorithm is introduced in each as an example for particular consideration.
19
Keuthen, Ralf. "Heuristic approaches for routing optimisation." Thesis, University of Nottingham, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.275960.
20
Garrett, Caelan Reed. "Heuristic search for manipulation planning." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100596.
Abstract:
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 61-64).
Manipulation problems involving many objects present substantial challenges for planning algorithms due to the high dimensionality and multi-modality of the search space. Symbolic task planners can efficiently construct plans involving many entities but cannot incorporate the constraints from geometry and kinematics. Existing approaches to integrated task and motion planning as well as manipulation planning remain prohibitively slow to plan in these high-dimensional hybrid configuration spaces involving many objects. We present the FFRoB algorithm for task and motion planning and the hybrid heuristic backward-forward (HHBF) planning algorithm for general manipulation planning. Both algorithms adapt heuristic ideas from one of the most successful symbolic planners in recent years, the FastForward (FF) planner, to continuous robotic planning domains. FFRoB discretizes task and motion planning problems using a multi-query roadmap structure that can be conditionalized to model different placements of movable objects. This structure enables the planner to efficiently compute the FFRoB heuristic which incorporates geometric and kinematic planning constraints to give a tight estimate of the distance to the goal. The resulting tightly integrated planner is simple and performs efficiently in a collection of tasks involving manipulation of many objects. HHBF generalizes this idea to planning with arbitrary manipulation primitives. It dynamically searches forward from the initial state towards the goal but uses a backward search from the goal, based on a simplified representation of the actions, to bias the sampling of the infinite action space towards action that are likely to be useful in reaching the goal. As a result, it can construct long manipulation plans in a large class of manipulation domains even more effectively than FFRoB. For both algorithms, we empirically demonstrate their effectiveness on complex manipulation tasks.
by Caelan Reed Garrett.
M. Eng.
21
Collether, John. "Portfolio optimization by heuristic algorithms." Thesis, University of Essex, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.635985.
Abstract:
Portfolio optimization is a major activity in business. It is intensively studied by researchers. Conventional portfolio optimization research made simplifying assumptions. For example, they assumed no constraint in how many assets one holds (cardinality constraint). They also assume no minimum and maximum holding sizes (holding size constraint). Once these assumptions are relaxed, conventional methods become inapplicable. New methods are demanded. Threshold Accepting is an established algorithm in the extended portfolio optimization problem.
22
Vella, Alan. "Hyper-heuristic decision tree induction." Thesis, Heriot-Watt University, 2012. http://hdl.handle.net/10399/2540.
Abstract:
A hyper-heuristic is any algorithm that searches or operates in the space of heuristics as opposed to the space of solutions. Hyper-heuristics are increasingly used in function and combinatorial optimization. Rather than attempt to solve a problem using a fixed heuristic, a hyper-heuristic approach attempts to find a combination of heuristics that solve a problem (and in turn may be directly suitable for a class of problem instances). Hyper-heuristics have been little explored in data mining. This work presents novel hyper-heuristic approaches to data mining, by searching a space of attribute selection criteria for decision tree building algorithm. The search is conducted by a genetic algorithm. The result of the hyper-heuristic search in this case is a strategy for selecting attributes while building decision trees. Most hyper-heuristics work by trying to adapt the heuristic to the state of the problem being solved. Our hyper-heuristic is no different. It employs a strategy for adapting the heuristic used to build decision tree nodes according to some set of features of the training set it is working on. We introduce, explore and evaluate five different ways in which this problem state can be represented for a hyper-heuristic that operates within a decisiontree building algorithm. In each case, the hyper-heuristic is guided by a rule set that tries to map features of the data set to be split by the decision tree building algorithm to a heuristic to be used for splitting the same data set. We also explore and evaluate three different sets of low-level heuristics that could be employed by such a hyper-heuristic. This work also makes a distinction between specialist hyper-heuristics and generalist hyper-heuristics. The main difference between these two hyperheuristcs is the number of training sets used by the hyper-heuristic genetic algorithm. Specialist hyper-heuristics are created using a single data set from a particular domain for evolving the hyper-heurisic rule set. Such algorithms are expected to outperform standard algorithms on the kind of data set used by the hyper-heuristic genetic algorithm. Generalist hyper-heuristics are trained on multiple data sets from different domains and are expected to deliver a robust and competitive performance over these data sets when compared to standard algorithms. We evaluate both approaches for each kind of hyper-heuristic presented in this thesis. We use both real data sets as well as synthetic data sets. Our results suggest that none of the hyper-heuristics presented in this work are suited for specialization – in most cases, the hyper-heuristic’s performance on the data set it was specialized for was not significantly better than that of the best performing standard algorithm. On the other hand, the generalist hyper-heuristics delivered results that were very competitive to the best standard methods. In some cases we even achieved a significantly better overall performance than all of the standard methods.
23
Li, Jian. "Ensemble clustering via heuristic optimisation." Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/7510.
Abstract:
Traditional clustering algorithms have different criteria and biases, and there is no single algorithm that can be the best solution for a wide range of data sets. This problem often presents a significant obstacle to analysts in revealing meaningful information buried among the huge amount of data. Ensemble Clustering has been proposed as a way to avoid the biases and improve the accuracy of clustering. The difficulty in developing Ensemble Clustering methods is to combine external information (provided by input clusterings) with internal information (i.e. characteristics of given data) effectively to improve the accuracy of clustering. The work presented in this thesis focuses on enhancing the clustering accuracy of Ensemble Clustering by employing heuristic optimisation techniques to achieve a robust combination of relevant information during the consensus clustering stage. Two novel heuristic optimisation-based Ensemble Clustering methods, Multi-Optimisation Consensus Clustering (MOCC) and K-Ants Consensus Clustering (KACC), are developed and introduced in this thesis. These methods utilise two heuristic optimisation algorithms (Simulated Annealing and Ant Colony Optimisation) for their Ensemble Clustering frameworks, and have been proved to outperform other methods in the area. The extensive experimental results, together with a detailed analysis, will be presented in this thesis.
24
Stefanello, Fernando. "Heuristic approaches for network problems." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/134424.
Abstract:
Em nosso mundo altamente conectado, novas tecnologias provêm contínuas mudanças na velocidade e eficiência das redes de telecomunicações e de transporte. Muitas dessas tecnologias são originárias de pesquisas em problemas de otimização em redes aplicadas a diferentes áreas. Nesta tese, investigamos três problemas de otimização combinatória que podem ser abordados como estruturas de redes. Primeiramente, são abordados problemas de engenharia de tráfego em redes de transporte. O objetivo principal é investigar os efeitos de alterar o custo de um subconjunto de arcos da rede, considerando que os clientes desta rede agem com um comportamento bem definido. O objetivo é controlar o fluxo na rede de modo a obter uma melhor distribuição do fluxo, minimizando o congestionamento ou maximizando o fluxo em um subconjunto de arestas. No primeiro problema considerase instalar um número fixo de postos de pedágios e definir os valores das tarifas para minimizar o tempo médio de viagem dos usuários. No segundo problema abordado, o objetivo é definir os valores das tarifas para maximizar a receita arrecadada nos arcos com pedágios. Em ambos os problemas, os usuários escolhem as rotas com base nos caminhos de menor custo da origem para o destino. Em redes de telecomunicações, um problema de alocação sujeito às condições da rede é considerado. O objetivo é alocar um conjunto de recursos, minimizando o custo de comunicação. Uma aplicação de computação em nuvem é considerada, onde os recursos são máquinas virtuais que devem ser alocadas em um conjunto de centros de dados. Condições da rede como largura de banda e latência são consideradas de modo a garantir a qualidade dos serviços. Para todos estes problemas, os modelos matemáticos são apresentados e avaliados usando um solver comercial de propósito geral como um método exato. Além disso, abordagens heurísticas são propostas, incluindo uma classe de algoritmo genético de chaves aleatórias viciadas (BRKGA). Resultados experimentais demonstram o bom desempenho das abordagens heurísticas propostas, mostrando que o BRKGA é uma ferramenta eficiente para resolver diferentes tipos de problemas de otimização combinatória, especialmente sobre estruturas de rede.In our highly connected world, new technologies provide continuous changes in the speed and efficiency of telecommunication and transportation networks. Many of these technologies come from research on network optimization problems with applications in different areas. In this thesis, we investigate three combinatorial optimization problems that arise from optimization on networks. First, traffic engineering problems in transportation networks are addressed. The main objective is to investigate the effects of changing the cost of some links in the network regarding some well-defined user behavior. The goal is to control the flow in the network and seek a better flow distribution over the network and then minimize the traffic congestion or maximize the flow on a subset of links over network conditions. The first problem considered is to install a fixed number of tollbooths and define the values of tariffs to minimize the average user travel time. The second problem considered is to define the values of tariffs to maximize the revenue collected in the tolled arcs. In both problems, users choose the routes based on the least cost paths from source to destination. From telecommunication networks, a placement problem subjected to network conditions is considered. The main objective is to place a set of resources minimizing the communication cost. An application from cloud computing is considered, where the resources are virtual machines that should be placed in a set of data centers. Network conditions, such as bandwidth and latency, are considered in order to ensure the service quality. For all these problems, mathematical models are presented and evaluated using a general-purpose commercial solver as an exact method. Furthermore, new heuristics approaches are proposed, including some based on biased random-key genetic algorithm (BRKGA). Experimental results demonstrate the good performance of the proposed heuristic approaches, showing that BRKGA is an efficient tool for solving different kinds of combinatorial optimization problems, especially over network structures.
25
Chong, Yen N. "Heuristic algorithms for routing problems." Thesis, Curtin University, 2001. http://hdl.handle.net/20.500.11937/1090.
Abstract:
General routing problems deal with transporting some commodities and/or travelling along the axes of a given network in some optimal manner. In the modern world such problems arise in several contexts such as distribution of goods, transportation of commodities and/or people etc.In this thesis we focus on two classical routing problems, namely the Travelling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP). The TSP can be described as a salesman travels from his home city, visits each of the other ( n - 1) cities exactly once and returns back to the home city such that the total distance travelled by him is minimised. The VRP may be stated as follows: A set of n customers (with known locations and demands for some commodity) is to be supplied from a single depot using a set of delivery vehicles each with a prescribed capacity. A delivery route starts from the depot, visits some customers and returns back to the depot. The VRP is to determine the delivery routes for each vehicle such that the total distance travelled by all the vehicles is minimised.These routing problems are simple to state in terms of describing them in words. But they are very complex in terms of providing a suitable mathematical formulation and a valid procedure to solve them. These routing problems are simple to state in terms of describing them in words. But they are very complex in terms of providing a suitable mathematical formulation and a valid procedure to solve them. These problems belong to the class of NP-hard (Non- deterministic Polynomial) problems. With the present knowledge, it is believed that the problems in NP-hard are unlikely to have any good algorithms to arrive at optimal solutions to a general problem. Hence researchers have focused their effort on; (i) developing exact algorithms to solve as large size problems as possible; (ii) developing heuristics to produce near optimal solutions.The exact algorithms for such problems have not performed satisfactorily as they need an enormous amount of computational time to solve moderate size problems. For instance, in the literature, TSP of size 225-city, 4461-city and 7397-city were solved using computational time of 1 year, 1.9 years and 4 years respectively (Junger et al., 1995). Thus heuristics, in particular the probabilistic methods such as tabu search, play a significant role in obtaining near optimal solutions. In the literature there is very little comparison between the various exact algorithms and heuristics. (Very often the real-life problems are too large and no optimal solution can be found in a reasonable time.)One of the problems with a probabilistic heuristic is that different implementations (runs) of the same probabilistic heuristic on a given problem may produce distinct solutions of different quality. Thus the desired quality and reproducibility of the solution cannot be ensured. Furthermore, the performance of the heuristics on the benchmark problems provide no Guarantee of the quality of solutions that can be obtained for the problem faced by a researcher. Most of the documentation on the performance of heuristics in literature problems provides no information regarding the computational effort (CPU time) spent in obtaining the claimed solution, reproducibility of the claimed solution and the hardware environment of the implementation. This thesis focuses on some of these deficiencies.Most of the heuristics for general combinatorial optimisation problems are based on neighbourhood search methods. This thesis explores and provides a formal setup for defining neighbourhood structures, definitions of local optimum and global optimum. Furthermore it highlights the dependence and drawbacks of such methods on the neighbourhood structure.It is necessary to emphasise the need for a statistical analysis of the output to be part of any such probabilistic heuristic. Some of the statistical tools used for the two probabilistic heuristics for TSP and VRP are developed. Furthermore, these heuristics axe part of a bigger class called tabu search heuristics for combinatorial optimisation problems. Hence it includes an overview of the TSP, VRP and tabu search methods in Chapters 2, 3 and 4 respectively. Subsequently in Chapters 5, 6, 7 and 8 ideas of neighbourhood structure, local optimum etc. are developed and the required statistical analysis for some heuristics on the TSP and VRP is demonstrated. In Chapter 9, the conclusion of this thesis is drawn and the direction of future work is outlined. The following is a brief outline of the contribution of this thesis.In Chapter 5, the ideas of neighbourhood structure, local optimum, global optimum and probabilistic heuristics for any combinatorial optimisation problem sare developed. The drawbacks of the probabilistic heuristics for such problems axe highlighted. Furthermore, the need to select the best heuristic on the basis of testing a statistical hypothesis and related statistical analysis is emphasised.Chapter 6 illustrates some of the ideas presented in Chapter 5 using the GENIUS algorithm proposed for the TSP. Statistical analysis is performed for 36 variations of GENIUS algorithm based on different neighbourhood parameters, different types of insertion methods used and two types of constructions of starting solutions. The analysis is performed on 27 literature problems with size ranging from 100 cities to 532 cities and 20 randomly generated problems with size ranging from 100 cities to 480 cities. In both cases the best heuristic is selected. Furthermore, the fitting of the Weibull Distribution to the objective function values of the heuristic solutions provides an estimate of the optimal objective function value and a corresponding confidence interval for both the literature and randomly generated problems. In both cases the estimate of the optimal objective function values are within 8.2% of the best objective function value known.Since the GENIUS algorithm proved to be efficient, a hybrid heuristic for the TSP combining the branch and bound method and GENIUS algorithm to solve large dimensional problems is proposed. The algorithm is tested on both the literature problems with sizes ranging from 575 cities to 724 cities and randomly generated problems with sizes ranging from 500 cities to 700 cities. Though problems could not be solved to optimality within the 10 hours time limit, solutions were found within 2.4% of the best known objective function value in the literature.In Chapter 7, a similar statistical analysis for the TABUROUTE algorithm proposed for the VRP is conducted. The analysis is carried out based on the different neighbourhood parameters and tested using 14 literature problems with sizes ranging from 50 cities to 199 cities and 49 randomly generated problems with sizes ranging from 60 cities to 120 cities. In both sets of the problems, the statistical tests accepted the hypothesis that there is no significant difference in the solution produced between the various parameters used for the TABUROUTE algorithm. By fitting the Weibull distribution to the objective function values of the local optimal solutions, the optimal objective function value and a corresponding confidence intervals for each problem is estimated. These estimates give values that are to within 6.1% and 18.3% of the best known values for the literature problems and randomly generated problems respectively.In Chapter 8, the general neighbourhood search method for a general combinatorial optimisation problem is presented. Very often, the neighbourhood structure can be defined suitably only on a superset S of the set of feasible solutions S. Thus the solutions in SS are infeasible. Several questions axe posed regarding the computational complexity of the solution space of a problem. These concepts are illustrated on the 199-city CDVRP, using the TABUROUTE algorithm.In addition, the idea of complexity of the solution space based on the samples collected over the 140 runs is explored. Some of the data collected include the number of solutions with distance and/or capacity feasible, the number of feasible neighbourhood solutions encountered for one run, etc. Questions such asHow many solutions are there for the 199-city problem ?How many numbers of local minima solutions are there for the 199-city problem?What is the size of the feasible region for the 199-city problem?are answered. Finally, the conclusion is drawn that this problem cannot be used as a benchmark based on the size of the feasible region and too many local minima.The conclusion of this thesis and directions of future work are discussed in Chapter 9. There are two appendices presented at the end of the thesis. Appendix A presents the details of the Friedman test, the expected utility function test and the estimation of the optimal objective function value based on the Weibull distribution. Appendix B presents a list of tables from Chapters 6, 7 and 8.
26
Chong, Yen N. "Heuristic algorithms for routing problems." Curtin University of Technology, School of Mathematics and Statistics, 2001. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=12855.
Abstract:
General routing problems deal with transporting some commodities and/or travelling along the axes of a given network in some optimal manner. In the modern world such problems arise in several contexts such as distribution of goods, transportation of commodities and/or people etc.In this thesis we focus on two classical routing problems, namely the Travelling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP). The TSP can be described as a salesman travels from his home city, visits each of the other ( n - 1) cities exactly once and returns back to the home city such that the total distance travelled by him is minimised. The VRP may be stated as follows: A set of n customers (with known locations and demands for some commodity) is to be supplied from a single depot using a set of delivery vehicles each with a prescribed capacity. A delivery route starts from the depot, visits some customers and returns back to the depot. The VRP is to determine the delivery routes for each vehicle such that the total distance travelled by all the vehicles is minimised.These routing problems are simple to state in terms of describing them in words. But they are very complex in terms of providing a suitable mathematical formulation and a valid procedure to solve them. These routing problems are simple to state in terms of describing them in words. But they are very complex in terms of providing a suitable mathematical formulation and a valid procedure to solve them. These problems belong to the class of NP-hard (Non- deterministic Polynomial) problems. With the present knowledge, it is believed that the problems in NP-hard are unlikely to have any good algorithms to arrive at optimal solutions to a general problem. Hence researchers have focused their effort on; (i) developing exact algorithms to solve as large size problems as possible; (ii) developing heuristics to produce ++near optimal solutions.The exact algorithms for such problems have not performed satisfactorily as they need an enormous amount of computational time to solve moderate size problems. For instance, in the literature, TSP of size 225-city, 4461-city and 7397-city were solved using computational time of 1 year, 1.9 years and 4 years respectively (Junger et al., 1995). Thus heuristics, in particular the probabilistic methods such as tabu search, play a significant role in obtaining near optimal solutions. In the literature there is very little comparison between the various exact algorithms and heuristics. (Very often the real-life problems are too large and no optimal solution can be found in a reasonable time.)One of the problems with a probabilistic heuristic is that different implementations (runs) of the same probabilistic heuristic on a given problem may produce distinct solutions of different quality. Thus the desired quality and reproducibility of the solution cannot be ensured. Furthermore, the performance of the heuristics on the benchmark problems provide no Guarantee of the quality of solutions that can be obtained for the problem faced by a researcher. Most of the documentation on the performance of heuristics in literature problems provides no information regarding the computational effort (CPU time) spent in obtaining the claimed solution, reproducibility of the claimed solution and the hardware environment of the implementation. This thesis focuses on some of these deficiencies.Most of the heuristics for general combinatorial optimisation problems are based on neighbourhood search methods. This thesis explores and provides a formal setup for defining neighbourhood structures, definitions of local optimum and global optimum. Furthermore it highlights the dependence and drawbacks of such methods on the neighbourhood structure.It is necessary to emphasise ++
the need for a statistical analysis of the output to be part of any such probabilistic heuristic. Some of the statistical tools used for the two probabilistic heuristics for TSP and VRP are developed. Furthermore, these heuristics axe part of a bigger class called tabu search heuristics for combinatorial optimisation problems. Hence it includes an overview of the TSP, VRP and tabu search methods in Chapters 2, 3 and 4 respectively. Subsequently in Chapters 5, 6, 7 and 8 ideas of neighbourhood structure, local optimum etc. are developed and the required statistical analysis for some heuristics on the TSP and VRP is demonstrated. In Chapter 9, the conclusion of this thesis is drawn and the direction of future work is outlined. The following is a brief outline of the contribution of this thesis.In Chapter 5, the ideas of neighbourhood structure, local optimum, global optimum and probabilistic heuristics for any combinatorial optimisation problem sare developed. The drawbacks of the probabilistic heuristics for such problems axe highlighted. Furthermore, the need to select the best heuristic on the basis of testing a statistical hypothesis and related statistical analysis is emphasised.Chapter 6 illustrates some of the ideas presented in Chapter 5 using the GENIUS algorithm proposed for the TSP. Statistical analysis is performed for 36 variations of GENIUS algorithm based on different neighbourhood parameters, different types of insertion methods used and two types of constructions of starting solutions. The analysis is performed on 27 literature problems with size ranging from 100 cities to 532 cities and 20 randomly generated problems with size ranging from 100 cities to 480 cities. In both cases the best heuristic is selected. Furthermore, the fitting of the Weibull Distribution to the objective function values of the heuristic solutions provides an estimate of the ++
optimal objective function value and a corresponding confidence interval for both the literature and randomly generated problems. In both cases the estimate of the optimal objective function values are within 8.2% of the best objective function value known.Since the GENIUS algorithm proved to be efficient, a hybrid heuristic for the TSP combining the branch and bound method and GENIUS algorithm to solve large dimensional problems is proposed. The algorithm is tested on both the literature problems with sizes ranging from 575 cities to 724 cities and randomly generated problems with sizes ranging from 500 cities to 700 cities. Though problems could not be solved to optimality within the 10 hours time limit, solutions were found within 2.4% of the best known objective function value in the literature.In Chapter 7, a similar statistical analysis for the TABUROUTE algorithm proposed for the VRP is conducted. The analysis is carried out based on the different neighbourhood parameters and tested using 14 literature problems with sizes ranging from 50 cities to 199 cities and 49 randomly generated problems with sizes ranging from 60 cities to 120 cities. In both sets of the problems, the statistical tests accepted the hypothesis that there is no significant difference in the solution produced between the various parameters used for the TABUROUTE algorithm. By fitting the Weibull distribution to the objective function values of the local optimal solutions, the optimal objective function value and a corresponding confidence intervals for each problem is estimated. These estimates give values that are to within 6.1% and 18.3% of the best known values for the literature problems and randomly generated problems respectively.In Chapter 8, the general neighbourhood search method for a general combinatorial optimisation problem is presented. Very often, the neighbourhood structure ++
can be defined suitably only on a superset S of the set of feasible solutions S. Thus the solutions in SS are infeasible. Several questions axe posed regarding the computational complexity of the solution space of a problem. These concepts are illustrated on the 199-city CDVRP, using the TABUROUTE algorithm.In addition, the idea of complexity of the solution space based on the samples collected over the 140 runs is explored. Some of the data collected include the number of solutions with distance and/or capacity feasible, the number of feasible neighbourhood solutions encountered for one run, etc. Questions such asHow many solutions are there for the 199-city problem ?How many numbers of local minima solutions are there for the 199-city problem?What is the size of the feasible region for the 199-city problem?are answered. Finally, the conclusion is drawn that this problem cannot be used as a benchmark based on the size of the feasible region and too many local minima.The conclusion of this thesis and directions of future work are discussed in Chapter 9. There are two appendices presented at the end of the thesis. Appendix A presents the details of the Friedman test, the expected utility function test and the estimation of the optimal objective function value based on the Weibull distribution. Appendix B presents a list of tables from Chapters 6, 7 and 8.
27
Hong, Libin. "Hyper-heuristic approaches to automatically designing heuristics as mutation operators for evolutionary programming on function classes." Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/52348/.
Abstract:
A hyper-heuristic is a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Researchers classify hyper-heuristics according to the source of feedback during learning: Online learning hyper-heuristics learn while solving a given instance of a problem; Offline learning hyper-heuristics learn from a set of training instances, a method that can generalise to unseen instances. Genetic programming (GP) can be considered a specialization of the more widely known genetic algorithms (GAs) where each individual is a computer program. GP automatically generates computer programs to solve specified tasks. It is a method of searching a space of computer programs. GP can be used as a kind of hyper-heuristic to be a learning algorithm when it uses some feedback from the search process. Our research mainly uses genetic programming as offline hyper-heuristic approach to automatically design various heuristics for evolutionary programming.
28
Méllo, Fábio Gavião Avelino de [UNESP]. "Método heurístico para criação de linhas de trabalho em problemas de escalonamento de pessoal." Universidade Estadual Paulista (UNESP), 2014. http://hdl.handle.net/11449/106401.
Abstract:
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000757731.pdf: 2807037 bytes, checksum: 5aa196a9569db15583f96a6a1fbce2c8 (MD5)A presente pesquisa trata do desenvolvimento de um método de solução do problema de construção de linhas de trabalho para a área de escalonamento de pessoal. Foram investigados diversos artigos da área de programação de pessoal com o objetivo de escolher precisamente o tema da pesquisa. Este tema escolhido foi o da construção de linhas de trabalho para empresas de ônibus interurbanos no Brasil. De posse do tema escolhido, foram analisados os métodos usados para formular e resolver o problema. Como resultado foi decidido o uso de uma formulação de cobertura de conjuntos não unicusto para representar o problema em estudo e o uso de um método heurístico para resolver o mesmo. Esta heurística divide a solução do problema em duas fases. A primeira é a fase construtiva, em que o espaço de solução é montado e linhas de trabalho são investigadas e aquelas viáveis são agrupadas formando um conjunto de linhas viáveis e qualificadas. A segunda é a fase de otimização ou de busca local em que um algoritmo evolutivo, baseado em algoritmo genético, irá procurar a melhor solução dentro desse subconjunto de linhas viáveis e qualificadas obtidas na primeira fase. Estes dois procedimentos se repetem até que um critério de parada seja atingido. Testes computacionais foram realizados no sentido de demonstrar a eficácia e eficiência do método proposto. Em seguida, o problema da programação de dias de expediente e de folga, neste trabalho denominado problema de padrões de folga, é formulado e resolvido. Algumas propostas para integrar a solução do problema de criação de linhas de trabalho à do problema de padrões de folga são apresentadas e discutidas
This thesis deals with the development of a method for solving the problem of construction of lines of work for the application area of personnel scheduling. Several articles were analyzed in order to matching precisely the subject of the research. An in-depth review of the processes used for formulating and solving such a kind of problem in the literature was conducted. As a result, it was decided to formulating the problem as a non unicost set covering problem and to use a heuristic method to solve it. The proposed heuristic is a twofold algorithm. The first is the construction phase, in which the solution space is scanned and working lines are investigated and those feasible are grouped together forming a set of feasible and qualified lines. The second phase is the optimization or local search in which an evolutionary algorithm based on genetic algorithm will search for the best solution within this set of feasible and qualified lines obtained in the first phase. These two phases are repeated until a stop criterion is reached. Computational tests were performed to demonstrate the effectiveness and efficiency of the proposed method. Then, the tour scheduling problem is addressed in the context of finding shifts of work-days and days-off scheduling. Its resolved by deterministic techniques. Some methods are then discussed on how to integrating both of the solutions of the lines of work and the tour scheduling problems
29
Méllo, Fábio Gavião Avelino de. "Método heurístico para criação de linhas de trabalho em problemas de escalonamento de pessoal /." Guaratinguetá, 2014. http://hdl.handle.net/11449/106401.
Abstract:
Orientador: Edson Luiz França SenneBanca: Galeno José de Sena
Banca: José Roberto Dale Luche
Banca: Anibal Tavares de Azevedo
Banca: Kelly Cristina Poldi
Resumo : A presente pesquisa trata do desenvolvimento de um método de solução do problema de construção de linhas de trabalho para a área de escalonamento de pessoal. Foram investigados diversos artigos da área de programação de pessoal com o objetivo de escolher precisamente o tema da pesquisa. Este tema escolhido foi o da construção de linhas de trabalho para empresas de ônibus interurbanos no Brasil. De posse do tema escolhido, foram analisados os métodos usados para formular e resolver o problema. Como resultado foi decidido o uso de uma formulação de cobertura de conjuntos não unicusto para representar o problema em estudo e o uso de um método heurístico para resolver o mesmo. Esta heurística divide a solução do problema em duas fases. A primeira é a fase construtiva, em que o espaço de solução é montado e linhas de trabalho são investigadas e aquelas viáveis são agrupadas formando um conjunto de linhas viáveis e qualificadas. A segunda é a fase de otimização ou de busca local em que um algoritmo evolutivo, baseado em algoritmo genético, irá procurar a melhor solução dentro desse subconjunto de linhas viáveis e qualificadas obtidas na primeira fase. Estes dois procedimentos se repetem até que um critério de parada seja atingido. Testes computacionais foram realizados no sentido de demonstrar a eficácia e eficiência do método proposto. Em seguida, o problema da programação de dias de expediente e de folga, neste trabalho denominado problema de padrões de folga, é formulado e resolvido. Algumas propostas para integrar a solução do problema de criação de linhas de trabalho à do problema de padrões de folga são apresentadas e discutidas
Abstract: This thesis deals with the development of a method for solving the problem of construction of lines of work for the application area of personnel scheduling. Several articles were analyzed in order to matching precisely the subject of the research. An in-depth review of the processes used for formulating and solving such a kind of problem in the literature was conducted. As a result, it was decided to formulating the problem as a non unicost set covering problem and to use a heuristic method to solve it. The proposed heuristic is a twofold algorithm. The first is the construction phase, in which the solution space is scanned and working lines are investigated and those feasible are grouped together forming a set of feasible and qualified lines. The second phase is the optimization or local search in which an evolutionary algorithm based on genetic algorithm will search for the best solution within this set of feasible and qualified lines obtained in the first phase. These two phases are repeated until a stop criterion is reached. Computational tests were performed to demonstrate the effectiveness and efficiency of the proposed method. Then, the tour scheduling problem is addressed in the context of finding shifts of work-days and days-off scheduling. Its resolved by deterministic techniques. Some methods are then discussed on how to integrating both of the solutions of the lines of work and the tour scheduling problems
Doutor
30
Bergmark, Max. "Tetris: A Heuristic Study : Using height-based weighing functions and breadth-first search heuristics for playing Tetris." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168306.
Abstract:
This paper studies the performance of height-based weighing functions and compares the results to using the commonly used non height-based weighing functions for holes. For every test performed, the heuristic methods studied in this paper performed better than the commonly used heuristic function. This study also analyses the effect of adding levels of prediction to the heuristic algorithm, which increases the average number of cleared lines by a factor of 85 in total. Utilising these methods can provide increased performance for a Tetris AI. The polynomic weighing functions discussed in this paper provide a performance increase without increasing the needed computation, increasing the number of cleared lines by a factor of 3. The breadth-first search provide a bigger performance increase, but every new level of prediction requires 162 times more computation. Every level increases the number of cleared lines by a factor of 9 from what has been observed in this study.
31
Mathirajan, M. "Heuristic Scheduling Algorithms For Parallel Heterogeneous Batch Processors." Thesis, Indian Institute of Science, 2000. https://etd.iisc.ac.in/handle/2005/196.
Abstract:
In the last decade, market pressures for greater variety of products forced a gradual shift from continuous manufacturing to batch manufacturing in various industries. Consequently batch scheduling problems have attracted the attention of researchers in production and operations management.
This thesis addresses the scheduling of parallel non-identical batch processors in the presence of dynamic job arrivals, incompatible job-families and non-identical job sizes. This problem abstracts the scheduling of heat-treatment furnace operations of castings in a steel foundry. The problem is of considerable interest in this sector as a large proportion of the total production time is spent in heat treatment processing. This problem is also encountered in other industrial settings such as burn-in operation in the final testing stage of semiconductor manufacturing, and manufacturing of steel, ceramics, aircraft parts, footwear, etc. A detailed literature review and personal communications with experts revealed that this class of batch scheduling problems have not been addressed hitherto.
A major concern in the management of foundries is to maximize throughput and reduce flow time and work-in-process inventories. Therefore we have chosen the primary scheduling objective to be the utilization of batch processors and as secondary objectives the minimization of overall flow time and weighted average waiting time per job. This formulation can be considered as an extension of problems studied by DOBSON AND NAMBINADOM (1992), UZSOY (1995), ZEE et a/. (1997) and MEHTA AND UZSOY (1998). Our effort to carefully catalogue the large number of variants of deterministic batch scheduling problems led us to the development of a taxonomy and notation.
Not surprisingly, we are able to show that our problem is NP-hard and is therefore in the company of many scheduling problems that are difficult to solve. Initially two heuristic algorithms, one a mathematical programming based heuristic algorithm (MPHA) and the other a greedy heuristic algorithm were developed. Due to the computational overheads in the implementation of MPHA when compared with the greedy heuristic, we chose to focus on the latter as the primary scheduling methodology.
Preliminary experimentation led us to the observation that the performance of greedy heuristics depends critically on the selection of job-families. So eight variants of the greedy heuristic that differ mainly in the decision on "job-family selection" were proposed. These eight heuristics are basically two sets {Al, A2, A3, A4} and the modified (MAI, MA2, MA3, MA4}, which differ only on how the "job-family" index, weighted shortest processing time, is computed.
For evaluating the performance of the eight heuristics, computational experiments were carried out. The analysis of the experimental data is presented in two perspectives. The goal of the first perspective was to evaluate the absolute quality of the solutions obtained by the proposed heuristic algorithms when compared with estimated optimal solutions. The second perspective was to compare the relative performance of the proposed heuristics.
The test problems generated were designed to reflect real-world scheduling problems that we have observed in the steel-casting industry. Three important problem parameters for the test set generation are the number of jobs [n], job-priority [P], and job-family [F]. We considered 5 different levels for n, 2 different levels for P and 2 different levels for F. The test set reflects (i) the size of the jobs vary uniformly (ii) there are two batch processors and (iii) five incompatible job-families with different processing times. 15 problem instances were generated for each level of (n, P, and F).
Out of many procedures available in the literature for estimating optimal value for combinatorial optimization problems, we used the procedure based on Weibull distribution as discussed in Rardin and Uzsoy (2001). For each problem instance of the randomly generated 300 problem instances, 15 feasible solutions (i.e., the average utilization of batch processors (AUBP)) were obtained using "random decision rule for first two stages and using a "best-fit heuristic' for the last stage of the scheduling problem. These 15 feasible solutions were used to estimate the optimal value. The generated 15 feasible solutions are expected to provide the estimated optimal value of the problem instance with a very high probability.
Both average performance and worst-case performance of the heuristics indicated that, the heuristic algorithms A3 and A4, on the average yielded better utilization than the estimated optimal value. This indicates that the Weibull-based technique may have yielded conservative estimates of the optimal value. Further, the other heuristic algorithms found inferior solutions when compared with the estimated optimal value. But the deviations were very small. From this, we may infer that all the proposed heuristic algorithms are acceptable.
The relative evaluation of heuristics was in terms of both computational effort and the quality of the solution. For the heuristics, it was clear that the computational burden is low enough on the average to run all the proposed heuristics on each problem instance and select the best solution. Also, it is observed that any algorithm from the first set of {Al, A2, A3, and A4} takes more computational time than any one from the second set {MAI, MA2, MA3, and MA4}. Regarding solution quality, the following inferences were made:
٭ In general the heuristic algorithms are sensitive to the choice of problem factors with
respect to all the scheduling objectives.
٭ The three algorithms A3, MA4 and MAI are observed to be superior with respect to the scheduling objectives: maximizing average utilization of batch processors (AUBP),
minimization of overall flow time (OFT) and minimizing weighted average waiting time
(WAWT) respectively. Further, the heuristic algorithm MAI turns out to be the best
choice if we trade-off all three objectives AUBP, OFT and WAWT.
Finally we carried out simple sensitivity analyses experiments in order to understand the influence of some parameters of the scheduling on the performance of the heuristic algorithms. These were related to one at a time changes in (1) job-size distribution, (2) capacities of batch processors and (3) processing time of job-families. From the analyses it appears that there is an influence of changes in these input parameters. The results of the sensitivity analyses can be used to guide the selection of a heuristic for a particular combination of input parameters. For example, if we have to pick a single heuristic algorithm, then MAI is the best choice when considering the performance and the robustness indicated by the sensitivity analysis.
In summary, this thesis examined a problem arising in the scheduling of heat-treatment operations in the steel-casting industry. This problem was abstracted to a class of deterministic batch scheduling problems. We analyzed the computational complexity of this problem and showed that it is NP-hard and therefore unlikely to admit a scalable exact method. Eight variants of a fast greedy heuristic were designed to solve the scheduling problem of interest.
Extensive computational experiments were carried out to compare the performance of the heuristics with estimated optimal values (using the Weibull technique) and also for relative effectiveness and this showed that the heuristics are capable of consistently obtaining near-estimated) optimal solutions with very low computational burden for the solution of large scale problems. Finally, a comprehensive sensitivity analysis was carried out to study the influence of a few parameters, by changing them one at a time, on the performance of the heuristic algorithms. This type of analysis gives users some confidence in the robustness of the proposed heuristics.
32
Mathirajan, M. "Heuristic Scheduling Algorithms For Parallel Heterogeneous Batch Processors." Thesis, Indian Institute of Science, 2000. http://hdl.handle.net/2005/196.
Abstract:
In the last decade, market pressures for greater variety of products forced a gradual shift from continuous manufacturing to batch manufacturing in various industries. Consequently batch scheduling problems have attracted the attention of researchers in production and operations management.
This thesis addresses the scheduling of parallel non-identical batch processors in the presence of dynamic job arrivals, incompatible job-families and non-identical job sizes. This problem abstracts the scheduling of heat-treatment furnace operations of castings in a steel foundry. The problem is of considerable interest in this sector as a large proportion of the total production time is spent in heat treatment processing. This problem is also encountered in other industrial settings such as burn-in operation in the final testing stage of semiconductor manufacturing, and manufacturing of steel, ceramics, aircraft parts, footwear, etc. A detailed literature review and personal communications with experts revealed that this class of batch scheduling problems have not been addressed hitherto.
A major concern in the management of foundries is to maximize throughput and reduce flow time and work-in-process inventories. Therefore we have chosen the primary scheduling objective to be the utilization of batch processors and as secondary objectives the minimization of overall flow time and weighted average waiting time per job. This formulation can be considered as an extension of problems studied by DOBSON AND NAMBINADOM (1992), UZSOY (1995), ZEE et a/. (1997) and MEHTA AND UZSOY (1998). Our effort to carefully catalogue the large number of variants of deterministic batch scheduling problems led us to the development of a taxonomy and notation.
Not surprisingly, we are able to show that our problem is NP-hard and is therefore in the company of many scheduling problems that are difficult to solve. Initially two heuristic algorithms, one a mathematical programming based heuristic algorithm (MPHA) and the other a greedy heuristic algorithm were developed. Due to the computational overheads in the implementation of MPHA when compared with the greedy heuristic, we chose to focus on the latter as the primary scheduling methodology.
Preliminary experimentation led us to the observation that the performance of greedy heuristics depends critically on the selection of job-families. So eight variants of the greedy heuristic that differ mainly in the decision on "job-family selection" were proposed. These eight heuristics are basically two sets {Al, A2, A3, A4} and the modified (MAI, MA2, MA3, MA4}, which differ only on how the "job-family" index, weighted shortest processing time, is computed.
For evaluating the performance of the eight heuristics, computational experiments were carried out. The analysis of the experimental data is presented in two perspectives. The goal of the first perspective was to evaluate the absolute quality of the solutions obtained by the proposed heuristic algorithms when compared with estimated optimal solutions. The second perspective was to compare the relative performance of the proposed heuristics.
The test problems generated were designed to reflect real-world scheduling problems that we have observed in the steel-casting industry. Three important problem parameters for the test set generation are the number of jobs [n], job-priority [P], and job-family [F]. We considered 5 different levels for n, 2 different levels for P and 2 different levels for F. The test set reflects (i) the size of the jobs vary uniformly (ii) there are two batch processors and (iii) five incompatible job-families with different processing times. 15 problem instances were generated for each level of (n, P, and F).
Out of many procedures available in the literature for estimating optimal value for combinatorial optimization problems, we used the procedure based on Weibull distribution as discussed in Rardin and Uzsoy (2001). For each problem instance of the randomly generated 300 problem instances, 15 feasible solutions (i.e., the average utilization of batch processors (AUBP)) were obtained using "random decision rule for first two stages and using a "best-fit heuristic' for the last stage of the scheduling problem. These 15 feasible solutions were used to estimate the optimal value. The generated 15 feasible solutions are expected to provide the estimated optimal value of the problem instance with a very high probability.
Both average performance and worst-case performance of the heuristics indicated that, the heuristic algorithms A3 and A4, on the average yielded better utilization than the estimated optimal value. This indicates that the Weibull-based technique may have yielded conservative estimates of the optimal value. Further, the other heuristic algorithms found inferior solutions when compared with the estimated optimal value. But the deviations were very small. From this, we may infer that all the proposed heuristic algorithms are acceptable.
The relative evaluation of heuristics was in terms of both computational effort and the quality of the solution. For the heuristics, it was clear that the computational burden is low enough on the average to run all the proposed heuristics on each problem instance and select the best solution. Also, it is observed that any algorithm from the first set of {Al, A2, A3, and A4} takes more computational time than any one from the second set {MAI, MA2, MA3, and MA4}. Regarding solution quality, the following inferences were made:
٭ In general the heuristic algorithms are sensitive to the choice of problem factors with
respect to all the scheduling objectives.
٭ The three algorithms A3, MA4 and MAI are observed to be superior with respect to the scheduling objectives: maximizing average utilization of batch processors (AUBP),
minimization of overall flow time (OFT) and minimizing weighted average waiting time
(WAWT) respectively. Further, the heuristic algorithm MAI turns out to be the best
choice if we trade-off all three objectives AUBP, OFT and WAWT.
Finally we carried out simple sensitivity analyses experiments in order to understand the influence of some parameters of the scheduling on the performance of the heuristic algorithms. These were related to one at a time changes in (1) job-size distribution, (2) capacities of batch processors and (3) processing time of job-families. From the analyses it appears that there is an influence of changes in these input parameters. The results of the sensitivity analyses can be used to guide the selection of a heuristic for a particular combination of input parameters. For example, if we have to pick a single heuristic algorithm, then MAI is the best choice when considering the performance and the robustness indicated by the sensitivity analysis.
In summary, this thesis examined a problem arising in the scheduling of heat-treatment operations in the steel-casting industry. This problem was abstracted to a class of deterministic batch scheduling problems. We analyzed the computational complexity of this problem and showed that it is NP-hard and therefore unlikely to admit a scalable exact method. Eight variants of a fast greedy heuristic were designed to solve the scheduling problem of interest.
Extensive computational experiments were carried out to compare the performance of the heuristics with estimated optimal values (using the Weibull technique) and also for relative effectiveness and this showed that the heuristics are capable of consistently obtaining near-estimated) optimal solutions with very low computational burden for the solution of large scale problems. Finally, a comprehensive sensitivity analysis was carried out to study the influence of a few parameters, by changing them one at a time, on the performance of the heuristic algorithms. This type of analysis gives users some confidence in the robustness of the proposed heuristics.
33
Richards, Simon Kim. "Symbolic bidirectional breadth-first heuristic search." Master's thesis, Mississippi State : Mississippi State University, 2004. http://library.msstate.edu/etd/show.asp?etd=etd-08302004-085304.
34
Hodge, Bertram C. "A heuristic for land-attack predesignation." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1999. http://handle.dtic.mil/100.2/ADA374087.
Abstract:
Thesis (M.S. in Operations Research) Naval Postgraduate School, December 1999."December 1999". Thesis advisor(s): Alexandra M. Newman. Includes bibliographical references (p. 47). Also available online.
35
Tian, Zhong Huan. "Gender based meta-heuristic optimization algorithms." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691331.
36
Asta, Shahriar. "Machine learning for improving heuristic optimisation." Thesis, University of Nottingham, 2015. http://eprints.nottingham.ac.uk/34216/.
Abstract:
Heuristics, metaheuristics and hyper-heuristics are search methodologies which have been preferred by many researchers and practitioners for solving computationally hard combinatorial optimisation problems, whenever the exact methods fail to produce high quality solutions in a reasonable amount of time. In this thesis, we introduce an advanced machine learning technique, namely, tensor analysis, into the field of heuristic optimisation. We show how the relevant data should be collected in tensorial form, analysed and used during the search process. Four case studies are presented to illustrate the capability of single and multi-episode tensor analysis processing data with high and low abstraction levels for improving heuristic optimisation. A single episode tensor analysis using data at a high abstraction level is employed to improve an iterated multi-stage hyper-heuristic for cross-domain heuristic search. The empirical results across six different problem domains from a hyper-heuristic benchmark show that significant overall performance improvement is possible. A similar approach embedding a multi-episode tensor analysis is applied to the nurse rostering problem and evaluated on a benchmark of a diverse collection of instances, obtained from different hospitals across the world. The empirical results indicate the success of the tensor-based hyper-heuristic, improving upon the best-known solutions for four particular instances. Genetic algorithm is a nature inspired metaheuristic which uses a population of multiple interacting solutions during the search. Mutation is the key variation operator in a genetic algorithm and adjusts the diversity in a population throughout the evolutionary process. Often, a fixed mutation probability is used to perturb the value at each locus, representing a unique component of a given solution. A single episode tensor analysis using data with a low abstraction level is applied to an online bin packing problem, generating locus dependent mutation probabilities. The tensor approach improves the performance of a standard genetic algorithm on almost all instances, significantly. A multi-episode tensor analysis using data with a low abstraction level is embedded into multi-agent cooperative search approach. The empirical results once again show the success of the proposed approach on a benchmark of flow shop problem instances as compared to the approach which does not make use of tensor analysis. The tensor analysis can handle the data with different levels of abstraction leading to a learning approach which can be used within different types of heuristic optimisation methods based on different underlying design philosophies, indeed improving their overall performance.
37
Cox, Donald Allan. "Supporting results synthesis in Heuristic Evaluation." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0020/MQ55279.pdf.
38
Liogys, Mindaugas. "Heuristic Algorithms for Nurse Rostering Problem." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2013. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2013~D_20130930_092436-21259.
Abstract:
In the dissertation the nurse rostering problem is investigated. The formulation of the problem is based on real-world data of one of the largest healthcare centers in Lithuania. Most recent publications that tackle the nurse rostering problem and the methods for solving the nurse rostering problem are reviewed, the mathematical formulation of the single objective and the multi-objective nurse rostering problem is presented, the requirements for the roster are described and a new method for solving the single objective and the multi-objective nurse rostering problem is proposed in this dissertation.Disertacijoje nagrinėjamas sveikatos priežiūros įstaigos darbuotojų darbų grafikų optimizavimo uždavinys, kuris formuluojamas ir sprendžiamas, remiantis vienos didžiausių Lietuvos sveikatos priežiūros įstaigų, realiais duomenimis. Disertacijoje apžvelgiami darbų grafikų optimizavimo uždaviniai bei jų sprendimo metodai. Pateikiama nagrinėjamo darbų grafikų vienakriterio ir daugiakriterio optimizavimo uždavinių matematinės formuluotės. Aprašomos sąlygos, kurias turi tenkinti sudaromasis darbų grafikas. Nagrinėjami metodai, tiek vienakriteriams, tiek daugiakriteriams darbų grafikų optimizavimo uždaviniams spręsti. Pasiūlytas naujas metodas, kuris yra efektyvesnis nei kiti nagrinėti metodai sprendžiant disertacijoje suformuluotą uždavinį.
39
Hassan, Atif Agha. "Information retrieval using advanced heuristic techniques." Thesis, University of Reading, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.515770.
40
Lewis, John N. "Expert systems development utilizing heuristic methods." Thesis, Monterey, California. Naval Postgraduate School, 1996. http://hdl.handle.net/10945/8376.
Abstract:
Approved for public release; distribution is unlimited.This thesis analyzes the diagnostic domain and isolates the heuristics employed by experts to arrive at diagnostic solutions. These heuristic methods are then generalized in order to arrive at a series of heuristic rules that can be applied to a wide range of diagnostic processes independent of there respective domain. To test the validity of the generalized heuristics, a prototype expert system was created targeting the heuristics employed by avionics repair technicians in repair of the APS- 1 15 radar system on the P-3C Orion.
41
El, Rhalibi Abdennour. "Hybrid heuristic techniques for rescheduling problems." Thesis, Liverpool John Moores University, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.431283.
42
Abdullah, Salwani. "Heuristic approaches for university timetabling problems." Thesis, University of Nottingham, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.428959.
43
Amir-Hussin, Amir A. B. "Heuristic methods for coalition structure generation." Thesis, Loughborough University, 2017. https://dspace.lboro.ac.uk/2134/26275.
Abstract:
The Coalition Structure Generation (CSG) problem requires finding an optimal partition of a set of n agents. An optimal partition means one that maximizes global welfare. Computing an optimal coalition structure is computationally hard especially when there are externalities, i.e., when the worth of a coalition is dependent on the organisation of agents outside the coalition. A number of algorithms were previously proposed to solve the CSG problem but most of these methods were designed for systems without externalities. Very little attention has been paid to finding optimal coalition structures in the presence of externalities, although externalities are a key feature of many real world multiagent systems. Moreover, the existing methods, being non-heuristic, have exponential time complexity which means that they are infeasible for any but systems comprised of a small number of agents. The aim of this research is to develop effective heuristic methods for finding optimal coalition structures in systems with externalities, where time taken to find a solution is more important than the quality of the solution. To this end, four different heuristics methods namely tabu search, simulated annealing, ant colony search and particle swarm optimisation are explored. In particular, neighbourhood operators were devised for the effective exploration of the search space and a compact representation method was formulated for storing details about the multiagent system. Using these, the heuristic methods were devised and their performance was evaluated extensively for a wide range of input data.
44
Pellicane, Jacqueline Marie. "Medical Art Therapy: A Heuristic Exploration." Digital Commons at Loyola Marymount University and Loyola Law School, 2011. https://digitalcommons.lmu.edu/etd/88.
Abstract:
Medical art therapy is a specific type of art therapy practiced primarily in settings where clients are actively ill or in recovery from a medical procedure. This heuristic study will seek to support the advancement of growth in this field, a wide spread use of medical art therapy in every setting catering to the medically or chronically ill. The researcher used her own medical records from a 10-year bout with illness, childhood to late adolescence, to stimulate the production of data in the form of journal entries and artwork. The data collected was then analyzed through both a clinical and personal lens to determine the existence of themes or patterns not only in the artwork, but also in the perceptions of the child then battling illness and now being assessed by their adult self. This research not only supports the benefits of utilizing art making/art therapy in processing and recovering from chronic illness but also in using the heuristic method of research to answer deeper questions from the perspectives of the clinician and the participant simultaneously.
45
Wang, Ning. "Model-Free Optimized Tracking Control Heuristic." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40911.
Abstract:
Tracking control algorithms often target the convergence of a tracking error. However, this can be at the expense of other important system characteristics, such as the control effort used to annihilate the tracking error, transient response, or steady-state characteristics, for example. Furthermore, most tracking control methods assume prior knowledge of the system dynamics, which is not always a realistic assumption, especially in the case of highly complex systems.
In this thesis, a model-free optimized tracking control architectural heuristic is proposed. The suggested feedback system is composed of two control loops. The first is the tracking loop. It focuses on the convergence of the tracking error. It is implemented using two different model-free control algorithms for comparison purpose: Reinforcement Learning (RL) and the Nonlinear Threshold Accepting (NLTA) technique. The RL scheme reformulates the tracking error combinations into a form of Markov-Decision-Process (MDP) and applies Q-Learning to build the best tracking control policy for the dynamic system under consideration. On the other hand, the NLTA algorithm is applied to tune the gains of a PID controller. The second control loop is in the form of a nonlinear state feedback loop. It is implemented using a feedforward artificial neural network (ANN) to optimize a system-wide cost function which can be flexible enough to encompass a set of desired design requirements pertaining to the targeted system behavior. This may include, for instance, the target overshoot, settling time, rise time, etc. The proposed architectural heuristic provides a model-free framework to tackle such control problems, in the sense that the plant's dynamic model is not required to be known in advance. Yet, at least a subset of the stability region of the optimized gains has to be known in advance so that it can provide a search space for the optimization algorithms. Simulation results on two dynamic systems demonstrate the superiority of the proposed control scheme.
46
Chua, Eng Hong. "Determine network survivability using heuristic models." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Mar%5FChua%5FEngHong.pdf.
47
Holmes, Stephen Terry. "Heuristic generation of software test data." Thesis, University of South Wales, 1996. https://pure.southwales.ac.uk/en/studentthesis/heuristic-generation-of-software-test-data(aa20a88e-32a5-4958-9055-7abc11fbc541).html.
Abstract:
Incorrect system operation can, at worst, be life threatening or financially devastating. Software testing is a destructive process that aims to reveal software faults. Selection of good test data can be extremely difficult. To ease and assist test data selection, several test data generators have emerged that use a diverse range of approaches. Adaptive test data generators use existing test data to produce further effective test data. It has been observed that there is little empirical data on the adaptive approach. This thesis presents the Heuristically Aided Testing System (HATS), which is an adaptive test data generator that uses several heuristics. A heuristic embodies a test data generation technique. Four heuristics have been developed. The first heuristic, Direct Assignment, generates test data for conditions involving an input variable and a constant. The Alternating Variable heuristic determines a promising direction to modify input variables, then takes ever increasing steps in this direction. The Linear Predictor heuristic performs linear extrapolations on input variables. The final heuristic, Boundary Follower, uses input domain boundaries as a guide to locate hard-to-find solutions. Several Ada procedures have been tested with HATS; a quadratic equation solver, a triangle classifier, a remainder calculator and a linear search. Collectively they present some common and rare test data generation problems. The weakest testing criterion HATS has attempted to satisfy is all branches. Stronger, mutation-based criteria have been used on two of the procedures. HATS has achieved complete branch coverage on each procedure, except where there is a higher level of control flow complexity combined with non-linear input variables. Both branch and mutation testing criteria have enabled a better understanding of the test data generation problems and contributed to the evolution of heuristics and the development of new heuristics. This thesis contributes the following to knowledge: Empirical data on the adaptive heuristic approach to test data generation. How input domain boundaries can be used as guidance for a heuristic. An effective heuristic termination technique based on the heuristic's progress. A comparison of HATS with random testing. Properties of the test software that indicate when HATS will take less effort than random testing are identified.
48
Clark, Calum. "Scheduling chemotherapy appointments : a heuristic approach." Thesis, University of Leeds, 2010. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.538443.
49
Samson, Duncan Alistair. "The heuristic significance of enacted visualisation." Thesis, Rhodes University, 2012. http://hdl.handle.net/10962/d1003434.
Abstract:
This study is centred on an analysis of pupils' lived experience while engaged in the generalisation of linear sequences/progressions presented in a pictorial context. The study is oriented within the conceptual framework of qualitative research, and is anchored within an interpretive paradigm. A case study methodological strategy was adopted, the research participants being the members of a mixed gender, high ability Grade 9 class of 23 pupils at an independent school in South Africa. The analytical framework is structured around a combination of complementary multiple perspectives provided by three theoretical ideas, enactivism, figural apprehension, and knowledge objectification. An important aspect of this analytical framework is the sensitivity it shows to the visual, phenomenological and semiotic aspects of figural pattern generalisation. It is the central thesis of this study that the combined complementary multiple perspectives of enactivism, figural apprehension and knowledge objectification provide a powerful depth of analysis to the exploration of the inter-relationship between the embodied processes of pattern generalisation and the visualisation of pictorial cues. The richly textured tapestry of activity captured through a multi-systemic semiotic analysis of participants' generalisation activity stands testament to this central thesis. Insights gleaned from this study are presented as practical strategies which support and encourage a multiple representational approach to pattern generalisation in the pedagogical context of the classroom.
50
King, Jesse Stocker 1982. "The Affect Heuristic in Consumer Evaluations." Thesis, University of Oregon, 2011. http://hdl.handle.net/1794/11530.
Abstract:
xv, 145 p. : ill. (some col.)This dissertation examines the role of affect in consumer judgments in two essays. The first essay explores the use of affect as a heuristic basis for judgments of the risks and benefits associated with new products. Current perspectives regarding the processes by which consumers make decisions about the adoption of innovations maintain that it is largely a cognitive process. However, the four studies that make up the first essay suggest that consumer assessments of the risks and benefits associated with product innovations are often inversely related and affectively congruent with evaluations of those innovations. The results support and extend previous research that has investigated the affect heuristic in the context of social hazards. The findings further indicate that more affectively extreme evaluations are associated with increasingly disparate assessments of risk and benefit. The results indicate that this relationship is consistent across a variety of products and product categories. Together, these findings challenge traditional conceptualizations of innovation adoption decision making and suggest that cognitive models alone are insufficient to explain innovation adoption decisions. The second essay investigates if processing fluency - the difficulty associated with processing information - may serve as an input to the affect heuristic and subsequent judgments of risk and benefit. Recently, Song and Schwarz investigated the relationship between differences in fluency and perceptions of risk. Their results suggested that fluency experiences influence risk perception through differences in familiarity and not as the result of fluency-elicited affect. The three studies included in the second essay re-examine those results in an effort to clarify the role of affect as a basis for perceptions of risk. The findings document a previously unreported reversal in preference for less fluent stimuli and suggest that fluency-elicited affect can explain the relationship between processing experiences and perceptions of risk. The results have important theoretical implications for our understanding of how people derive meaning from fluency experiences and for the role of fluency-elicited affect as a basis for judgments of risk and benefit.
Committee in charge: David Boush, Chairperson; Robert Madrigal, Member; Joan Giese, Member; Paul Slovic, Outside Member