Relevant bibliographies by topics / Heuristic

Academic literature on the topic 'Heuristic'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "Heuristic":

1

Sanggala, Ekra, and Muhammad Ardhya Bisma. "Perbandingan Savings Algorithm dengan Nearest Neighbour dalam Menyelesaikan Russian TSP Instances." Jurnal Media Teknik dan Sistem Industri 7, no. 1 (March 31, 2023): 27. http://dx.doi.org/10.35194/jmtsi.v7i1.3039.

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Travelling Salesman Problem (TSP) is the problem for finding the shortest route starting from start node then visiting number of nodes exactly once and finally go back to start node. Several heuristics are popular for solving TSP, for example Savings Algorithm and Nearest Neighbour. Performance heuristics on solving TSP are diverse, so there is need of reference for choosing a heuristic. Comparing heuristics on solving instance can be a reference for choosing a heuristic. This paper will discuss about comparison Savings Algorithm and Nearest Neighbour on Solving Russian TSP Instances. For generating length of route, Savings Algorithm is better than Nearest Neighbour, while for generating CPU time, Nearest Neighbour is better than Savings Algorithm. Travelling Salesman Problem (TSP) merupakan permasalahan penentuan rute terpendek yang diawali dari titik start untuk mengunjungi sekumpulan titik tepat sekali dan diakhiri dengan kembali ke titik start. Beberapa Heuristik yang cukup populer untuk menyelesaikan TSP antara lain Savings Algorithm dan Nearest Neighbour. Kemampuan Heuristik dalam menyelesaikan TSP berbeda-beda, sehingga diperlukan sebuah acuan untuk menentukan Heuristik yang akan digunakan. Membandingkan Heuristik dalam menyelesaikan instance dapat menjadi acuan untuk pemilihan Heuristik. Pada paper ini akan dibahas mengenai perbandingan Savings Algorithm dan Nearest Neighbour dalam menyelesaikan Russian TSP Instances. Untuk panjang rute yang dihasilkan, maka Savings Algorithm lebih baik dibandingkan Nearest Neighbour, sedangkan untuk CPU Time yang dihasilkan, maka Nearest Neighbour lebih baik dibandingkan Savings Algorithm.
2

Seipp, Jendrik. "Better Orders for Saturated Cost Partitioning in Optimal Classical Planning." Proceedings of the International Symposium on Combinatorial Search 8, no. 1 (September 1, 2021): 149–53. http://dx.doi.org/10.1609/socs.v8i1.18438.

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Cost partitioning is a general method for adding multiple heuristic values admissibly. In the setting of optimal classical planning, saturated cost partitioning has recently been shown to be the cost partitioning algorithm of choice for pattern database heuristics found by hill climbing, systematic pattern database heuristics and Cartesian abstraction heuristics. To evaluate the synergy of the three heuristic types, we compute the saturated cost partitioning over the combined sets of heuristics and observe that the resulting heuristic is outperformed by the heuristic that simply maximizes over the three saturated cost partitioning heuristics computed separately for each heuristic type. Our new algorithm for choosing the orders in which saturated cost partitioning considers the heuristics allows us to compute heuristics outperforming not only the maximizing heuristic but even state-of-the-art planners.
3

Wilt, Christopher, and Wheeler Ruml. "Effective Heuristics for Suboptimal Best-First Search." Journal of Artificial Intelligence Research 57 (October 31, 2016): 273–306. http://dx.doi.org/10.1613/jair.5036.

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Suboptimal heuristic search algorithms such as weighted A* and greedy best-first search are widely used to solve problems for which guaranteed optimal solutions are too expensive to obtain. These algorithms crucially rely on a heuristic function to guide their search. However, most research on building heuristics addresses optimal solving. In this paper, we illustrate how established wisdom for constructing heuristics for optimal search can fail when considering suboptimal search. We consider the behavior of greedy best-first search in detail and we test several hypotheses for predicting when a heuristic will be effective for it. Our results suggest that a predictive characteristic is a heuristic's goal distance rank correlation (GDRC), a robust measure of whether it orders nodes according to distance to a goal. We demonstrate that GDRC can be used to automatically construct abstraction-based heuristics for greedy best-first search that are more effective than those built by methods oriented toward optimal search. These results reinforce the point that suboptimal search deserves sustained attention and specialized methods of its own.
4

Ursani, Ziauddin, and David W. Corne. "Introducing Complexity Curtailing Techniques for the Tour Construction Heuristics for the Travelling Salesperson Problem." Journal of Optimization 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/4786268.

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In this paper, complexity curtailing techniques are introduced to create faster version of insertion heuristics, that is, cheapest insertion heuristic (CIH) and largest insertion heuristic (LIH), effectively reducing their complexities fromO(n3)toO(n2)with no significant effect on quality of solution. This paper also examines relatively not very known heuristic concept of max difference and shows that it can be culminated into a full-fledged max difference insertion heuristic (MDIH) by defining its missing steps. Further to this the paper extends the complexity curtailing techniques to MDIH to create its faster version. The resultant heuristic, that is, fast max difference insertion heuristic (FMDIH), outperforms the “farthest insertion” heuristic (FIH) across a wide spectrum of popular datasets with statistical significance, even though both the heuristics have the same worst case complexity ofO(n2). It should be noted that FIH is considered best among lowest order complexity heuristics. The complexity curtailing techniques presented here open up the new area of research for their possible extension to other heuristics.
5

Özcan, Ender, Mustafa Misir, Gabriela Ochoa, and Edmund K. Burke. "A Reinforcement Learning - Great-Deluge Hyper-Heuristic for Examination Timetabling." International Journal of Applied Metaheuristic Computing 1, no. 1 (January 2010): 39–59. http://dx.doi.org/10.4018/jamc.2010102603.

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Hyper-heuristics can be identified as methodologies that search the space generated by a finite set of low level heuristics for solving search problems. An iterative hyper-heuristic framework can be thought of as requiring a single candidate solution and multiple perturbation low level heuristics. An initially generated complete solution goes through two successive processes (heuristic selection and move acceptance) until a set of termination criteria is satisfied. A motivating goal of hyper-heuristic research is to create automated techniques that are applicable to a wide range of problems with different characteristics. Some previous studies show that different combinations of heuristic selection and move acceptance as hyper-heuristic components might yield different performances. This study investigates whether learning heuristic selection can improve the performance of a great deluge based hyper-heuristic using an examination timetabling problem as a case study.
6

Kuroiwa, Ryo, Alexander Shleyfman, Chiara Piacentini, Margarita P. Castro, and J. Christopher Beck. "LM-cut and Operator Counting Heuristics for Optimal Numeric Planning with Simple Conditions." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 210–18. http://dx.doi.org/10.1609/icaps.v31i1.15964.

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We consider optimal numeric planning with numeric conditions consisting of linear expressions of numeric state variables and actions that increase or decrease numeric state variables by constant quantities. We build on previous research to introduce a new variant of the numeric hmax heuristic based on the delete-relaxed version of such planning tasks. Although our hmax heuristic is inadmissible, it yields a numeric version of the classical LM-cut heuristic which is admissible. Further, we prove that our LM-cut heuristic neither dominates nor is dominated by the existing numeric heuristic hmax(hbd). We show that admissibility also holds when integrating the numeric cuts into the operator-counting (OC) heuristic producing an admissible numeric version of the OC heuristic. Through experiments, we demonstrate that both these heuristics compete favorably with the state-of-the-art heuristics: in particular, while sometimes expanding more nodes than other heuristics, numeric OC solves 19 more problem instances than the next closest heuristic.
7

BOUZY, BRUNO. "HISTORY AND TERRITORY HEURISTICS FOR MONTE CARLO GO." New Mathematics and Natural Computation 02, no. 02 (July 2006): 139–46. http://dx.doi.org/10.1142/s1793005706000427.

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Recently, the Monte Carlo approach has been applied to computer go with promising success. INDIGO uses such an approach which can be enhanced with specific heuristics. This paper assesses two heuristics within the 19 × 19 Monte Carlo go framework of INDIGO: the territory heuristic and the history heuristic, both in their internal and external versions. The external territory heuristic is more effective, leading to a 40-point improvement on 19 × 19 boards. The external history heuristic brings about a 10-point improvement. The internal territory heuristic yields a few points improvement, and the internal history heuristic has already been assessed on 19 × 19 boards in previous publications. Most of these heuristics were used by INDIGO at the 2004 Computer Olympiad.
8

Wilt, Christopher, and Wheeler Ruml. "Speedy Versus Greedy Search." Proceedings of the International Symposium on Combinatorial Search 5, no. 1 (September 1, 2021): 184–92. http://dx.doi.org/10.1609/socs.v5i1.18320.

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In work on satisficing search, there has been substantial attention devoted to how to solve problems associated with local minima or plateaus in the heuristic function. One technique that has been shown to be quite promising is using an alternative heuristic function that does not estimate cost-to-go, but rather estimates distance-to-go. Empirical results generally favor using the distance-to-go heuristic over the cost-to-go heuristic, but there is currently little beyond intuition to explain the difference. We begin by empirically showing that the success of the distance-to-go heuristic appears related to its having smaller local minima. We then discuss a reasonable theoretical model of heuristics and show that, under this model, the expected size of local minima is higher for a cost- to-go heuristic than a distance-to-go heuristic, offering a possible explanation as to why distance-to-go heuristics tend to outperform cost-to-go heuristics.
9

Speck, David, André Biedenkapp, Frank Hutter, Robert Mattmüller, and Marius Lindauer. "Learning Heuristic Selection with Dynamic Algorithm Configuration." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 597–605. http://dx.doi.org/10.1609/icaps.v31i1.16008.

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A key challenge in satisficing planning is to use multiple heuristics within one heuristic search. An aggregation of multiple heuristic estimates, for example by taking the maximum, has the disadvantage that bad estimates of a single heuristic can negatively affect the whole search. Since the performance of a heuristic varies from instance to instance, approaches such as algorithm selection can be successfully applied. In addition, alternating between multiple heuristics during the search makes it possible to use all heuristics equally and improve performance. However, all these approaches ignore the internal search dynamics of a planning system, which can help to select the most useful heuristics for the current expansion step. We show that dynamic algorithm configuration can be used for dynamic heuristic selection which takes into account the internal search dynamics of a planning system. Furthermore, we prove that this approach generalizes over existing approaches and that it can exponentially improve the performance of the heuristic search. To learn dynamic heuristic selection, we propose an approach based on reinforcement learning and show empirically that domain-wise learned policies, which take the internal search dynamics of a planning system into account, can exceed existing approaches.
10

Seipp, Jendrik, Florian Pommerening, and Malte Helmert. "New Optimization Functions for Potential Heuristics." Proceedings of the International Conference on Automated Planning and Scheduling 25 (April 8, 2015): 193–201. http://dx.doi.org/10.1609/icaps.v25i1.13714.

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Potential heuristics, recently introduced by Pommerening et al., characterize admissible and consistent heuristics for classical planning as a set of declarative constraints. Every feasible solution for these constraints defines an admissible heuristic, and we can obtain heuristics that optimize certain criteria such as informativeness by specifying suitable objective functions. The original paper only considered one such objective function: maximizing the heuristic value of the initial state. In this paper, we explore objectives that attempt to maximize heuristic estimates for all states (reachable and unreachable), maximize heuristic estimates for a sample of reachable states, maximize the number of detected dead ends, or minimize search effort. We also search for multiple heuristics with complementary strengths that can be combined to obtain even better heuristics.
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You might also be interested in the extended bibliographies on the topic 'Heuristic' for particular source types:
Journal articles Dissertations / Theses Books

Dissertations / Theses on the topic "Heuristic":

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.

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2

Ambrogi, Timothy. "Heuristic counterpoint." Diss., Connect to the thesis, 2004. http://hdl.handle.net/10066/1484.

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3

Murthy, Sapna Guniguntla. "Disaster recovery heuristic : a mapping heuristic for optimum retrieval /." Online version of thesis, 2009. http://hdl.handle.net/1850/10733.

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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.

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CENTRO DE PESQUISAS LEOPOLDO AMÉRICO MIGUEZ DE MELLO
Um 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.

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Monteith, Kristine Perry. "Heuristic Weighted Voting." BYU ScholarsArchive, 2007. https://scholarsarchive.byu.edu/etd/1206.

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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.

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Made available in DSpace on 2014-06-11T19:26:18Z (GMT). No. of bitstreams: 0 Previous issue date: 2012-10-26Bitstream added on 2014-06-13T19:33:57Z : No. of bitstreams: 1 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.

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Orientador: Galeno José de Sena
Banca: 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/.

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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.

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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.
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Books on the topic "Heuristic":

1

Salhi, Saïd. Heuristic Search. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49355-8.

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Ippoliti, Emiliano, ed. Heuristic Reasoning. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-09159-4.

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Tahin, Gábor. Heuristic Rhetoric. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98482-3.

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4

Nirmal, Arvind P. Heuristic explorations. Madras: Published for Gurukul Lutheran Theological College & Research Institute by Christian Literature Society, 1990.

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5

Clancey, William J. Heuristic classification. [Alexandria, Va.]: DTIC, 1985.

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Lappin, Gerald F. Heuristic symbolic integration. [S.l: The Author], 1987.

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7

Dasgupta, Pallab, P. P. Chakrabarti, and S. C. DeSarkar. Multiobjective Heuristic Search. Wiesbaden: Vieweg+Teubner Verlag, 1999. http://dx.doi.org/10.1007/978-3-322-86853-4.

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Moustakas, Clark E. Heuristic research: Design, methodology, and applications. Newbury Park: Sage Publications, 1990.

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Lee, Kwang Y., and Mohamed A. El-Sharkawi, eds. Modern Heuristic Optimization Techniques. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2008. http://dx.doi.org/10.1002/9780470225868.

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Hegeman, J. H. Justifying policy: A heuristic. Amsterdam: Free University Press, 1989.

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Book chapters on the topic "Heuristic":

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Pirrone, Angelo, Peter C. R. Lane, Laura Bartlett, Noman Javed, and Fernand Gobet. "Heuristic Search of Heuristics." In Artificial Intelligence XL, 407–20. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47994-6_36.

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Gass, Saul I., and Carl M. Harris. "heuristic." In Encyclopedia of Operations Research and Management Science, 731. New York, NY: Springer US, 2001. http://dx.doi.org/10.1007/1-4020-0611-x_905.

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Gass, Saul I., and Carl M. Harris. "heuristic." In Encyclopedia of Operations Research and Management Science, 731. New York, NY: Springer US, 2001. http://dx.doi.org/10.1007/1-4020-0611-x_906.

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Weik, Martin H. "heuristic." In Computer Science and Communications Dictionary, 721. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_8330.

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Xiang, Yao, and Yan Guoli. "Heuristic." In The ECPH Encyclopedia of Psychology, 1–2. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-6000-2_311-1.

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Salhi, Saïd. "Introduction." In Heuristic Search, 1–18. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49355-8_1.

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Salhi, Saïd. "Improvement-Only Heuristics." In Heuristic Search, 19–47. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49355-8_2.

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Salhi, Saïd. "Not Necessary Improving Heuristics." In Heuristic Search, 49–76. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49355-8_3.

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Salhi, Saïd. "Population-Based Heuristics." In Heuristic Search, 77–128. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49355-8_4.

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Salhi, Saïd. "Hybridisation Search." In Heuristic Search, 129–56. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49355-8_5.

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Conference papers on the topic "Heuristic":

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Hitomi, Nozomi, and Daniel Selva. "The Effect of Credit Definition and Aggregation Strategies on Multi-Objective Hyper-Heuristics." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47445.

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Heuristics and meta-heuristics are often used to solve complex real-world problems such as the non-linear, non-convex, and multi-objective combinatorial optimization problems that regularly appear in system design and architecture. Unfortunately, the performance of a specific heuristic is largely dependent on the specific problem at hand. Moreover, a heuristic’s performance can vary throughout the optimization process. Hyper-heuristics is one approach that can maintain relatively good performance over the course of an optimization process and across a variety of problems without parameter retuning or major modifications. Given a set of domain-specific and domain-independent heuristics, a hyper-heuristic adapts its search strategy over time by selecting the most promising heuristics to use at a given point. A hyper-heuristic must have: 1) a credit assignment strategy to rank the heuristics by their likelihood of producing improving solutions; and 2) a heuristic selection strategy based on the credits assigned to each heuristic. The literature contains many examples of hyper-heuristics with effective credit assignment and heuristic selection strategies for single-objective optimization problems. In multi-objective optimization problems, however, defining credit is less straightforward because there are often competing objectives. Therefore, there is a need to define and assign credit so that heuristics are rewarded for finding solutions with good trades between the objectives. This paper studies, for the first time, different combinations of credit definition, credit aggregation, and heuristic selection strategies. Credit definitions are based on different applications of the notion of Pareto dominance, namely: A1) dominance of the offspring with respect to the parent solutions; A2) ability to produce non-dominated solutions with respect to the entire population; A3) Pareto ranking with respect to the entire population. Two different credit aggregation strategies for assigning credit are also examined. A heuristic will receive credit for: B1) only the solutions it created in the current iteration or B2) all solutions it created that are in the current population. Different heuristic selection strategies are considered including: C1) probability matching; C2) dynamic multi-armed bandit; and C3) Hyper-GA. Thus, we conduct an experiment with three factors: credit definition (A1, A2, A3), credit aggregation (B1, B2), and heuristic selection (C1, C2, C3) and conduct a full factorial experiment. Performance is measured by hyper-volume of the last population. All algorithms are tested on a design problem for a climate monitoring satellite constellation instead of classical benchmarking problems to apply domain-specific heuristics within the hyper-heuristic.
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Puentes, Lucas, Jonathan Cagan, and Christopher McComb. "Automated Heuristic Induction From Human Design Data." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22151.

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Abstract Through experience, designers develop guiding principles, or heuristics, to aid decision-making in familiar design domains. Generalized versions of common design heuristics have been identified across multiple domains and applied by novices to design problems. Previous work leveraged a sample of these common heuristics to assist in an agent-based design process, which typically lacks heuristics. These predefined heuristics were translated into sequences of specifically applied design changes that followed the theme of the heuristic. To overcome the upfront burden, need for human interpretation, and lack of generality of this manual process, this paper presents a methodology that induces frequent heuristic sequences from an existing timeseries design change dataset. Individual induced sequences are then algorithmically grouped based on similarity to form groups that each represent a shared general heuristic. The heuristic induction methodology is applied to data from two human design studies in different design domains. The first dataset, collected from a truss design task, finds a highly similar set of general heuristics used by human designers to that which was hand selected for the previous computational agent study. The second dataset, collected from a cooling system design problem, demonstrates further applicability and generality of the heuristic induction process. Through this heuristic induction technique, designers working in a specified domain can learn from others’ prior problem-solving strategies and use these strategies in their own future design problems.
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Koriche, Frederic, Christophe Lecoutre, Anastasia Paparrizou, and Hugues Wattez. "Best Heuristic Identification for Constraint Satisfaction." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/258.

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In constraint satisfaction problems, the variable ordering heuristic takes a central place by selecting the variables to branch on during backtrack search. As many hand-crafted branching heuristics have been proposed in the literature, a key issue is to identify, from a pool of candidate heuristics, which one is the best for solving a given constraint satisfaction task. Based on the observation that modern constraint solvers are using restart sequences, the best heuristic identification problem can be cast in the context of multi-armed bandits as a non-stochastic best arm identification problem. Namely, during each run of some given restart sequence, the bandit algorithm selects a branching heuristic and receives a reward for this heuristic before proceeding to the next run. The goal is to identify the best heuristic using few runs, and without any stochastic assumption about the constraint solver. In this study, we propose an adaptive variant of Successive Halving that exploits Luby's universal restart sequence. We analyze the convergence of this bandit algorithm in the non-stochastic setting, and we demonstrate its empirical effectiveness on various constraint satisfaction benchmarks.
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Liang, Jia, Hari Govind, Pascal Poupart, Krzysztof Czarnecki, and Vijay Ganesh. "An Empirical Study of Branching Heuristics through the Lens of Global Learning Rate." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/745.

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In this paper, we analyze a suite of 7 well-known branching heuristics proposed by the SAT community and show that the better heuristics tend to generate more learnt clauses per decision, a metric we define as the global learning rate (GLR). We propose GLR as a metric for the branching heuristic to optimize. We test our hypothesis by developing a new branching heuristic that maximizes GLR greedily. We show empirically that this heuristic achieves very high GLR and interestingly very low literal block distance (LBD) over the learnt clauses. In our experiments this greedy branching heuristic enables the solver to solve instances faster than VSIDS, when the branching time is taken out of the equation. This experiment is a good proof of concept that a branching heuristic maximizing GLR will lead to good solver performance modulo the computational overhead. Finally, we propose a new branching heuristic, called SGDB, that uses machine learning to cheapily approximate greedy maximization of GLR. We show experimentally that SGDB performs on par with the VSIDS branching heuristic.
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Bercher, Pascal, Gregor Behnke, Daniel Höller, and Susanne Biundo. "An Admissible HTN Planning Heuristic." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/68.

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Hierarchical task network (HTN) planning is well-known for being an efficient planning approach. This is mainly due to the success of the HTN planning system SHOP2. However, its performance depends on hand-designed search control knowledge. At the time being, there are only very few domain-independent heuristics, which are designed for differing hierarchical planning formalisms. Here, we propose an admissible heuristic for standard HTN planning, which allows to find optimal solutions heuristically. It bases upon the so-called task decomposition graph (TDG), a data structure reflecting reachable parts of the task hierarchy. We show (both in theory and empirically) that rebuilding it during planning can improve heuristic accuracy thereby decreasing the explored search space. The evaluation further studies the heuristic both in terms of plan quality and coverage.
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Yokoyama, Soichiro, Ikuo Suzuki, Masahito Yamamoto, and Masashi Furukawa. "A New Heuristic for Traveling Salesman Problem Based on LCO." In ASME/ISCIE 2012 International Symposium on Flexible Automation. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/isfa2012-7227.

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The Traveling Salesman Problem (TSP) is one of the most well known combinatorial optimization problem and has wide range of application. Since the TSP is NP-hard, many heuristics for the TSP have been developed. This study proposes a new heuristic for the TSP based on one of these heuristics named Local Clustering Optimization (LCO). LCO is a metaheuristic proposed by Furukawa at el. to give an accurate solution for large scale problems in a reasonable time. However, conventional LCO-based heuristics for the TSP is not suited to solving asymmetric instances. The proposed method iteratively adopts tour construction heuristics such as nearest neighbor and random insertion to get an accurate solution more efficiently for the both asymmetric and symmetric TSP. The proposed method and other heuristics are applied to benchmark instances from TSPLIB and randomly generated instances. The experiment shows the proposed method is superior to conventional LCO in terms of accuracy of the solution. However, the proposed method is inefficient for instances which are not close to Euclidean due to the same property of insertion heuristic.
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Hu, Shuli, and Nathan R. Sturtevant. "Direction-Optimizing Breadth-First Search with External Memory Storage." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/175.

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While computing resources have continued to grow, methods for building and using large heuristics have not seen significant advances in recent years. We have observed that direction-optimizing breadth-first search, developed for and used broadly in the Graph 500 competition, can also be applied for building heuristics. But, the algorithm cannot run efficiently using external memory -- when the heuristics being built are larger than RAM. This paper shows how to modify direction-optimizing breadth-first search to build external-memory heuristics. We show that the new approach is not effective in state spaces with low asymptotic branching factors, but in other domains we are able to achieve up to a 3x reducing in runtime when building an external-memory heuristic. The approach is then used to build a 2.6TiB Rubik's Cube heuristic with 5.8 trillion entries, the largest pattern database heuristic ever built.
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Cohen, Liron, Tansel Uras, Shiva Jahangiri, Aliyah Arunasalam, Sven Koenig, and T. K. Satish Kumar. "The FastMap Algorithm for Shortest Path Computations." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/198.

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We present a new preprocessing algorithm for embedding the nodes of a given edge-weighted undirected graph into a Euclidean space. The Euclidean distance between any two nodes in this space approximates the length of the shortest path between them in the given graph. Later, at runtime, a shortest path between any two nodes can be computed with an A* search using the Euclidean distances as heuristic. Our preprocessing algorithm, called FastMap, is inspired by the data-mining algorithm of the same name and runs in near-linear time. Hence, FastMap is orders of magnitude faster than competing approaches that produce a Euclidean embedding using Semidefinite Programming. FastMap also produces admissible and consistent heuristics and therefore guarantees the generation of shortest paths. Moreover, FastMap applies to general undirected graphs for which many traditional heuristics, such as the Manhattan Distance heuristic, are not well defined. Empirically, we demonstrate that A* search using the FastMap heuristic is competitive with A* search using other state-of-the-art heuristics, such as the Differential heuristic.
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Renan de Carvalho, Vinicius, and Jaime Simão Sichman. "Multi-Agent Election-Based Hyper-Heuristics." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/833.

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Hyper-heuristics are high-level methodologies responsible for automatically discover how to combine elements from a low-level heuristic set in order to solve optimization problems. Agents, in turn, are autonomous component responsible for watching an environment and perform some actions according to their perceptions. Thus, agent-based techniques seem suitable for the design of hyper-heuristics. This work presents an agent-based hyper-heuristic framework for choosing the best low-level heuristic. The proposed framework performs a cooperative voting procedure, considering a set of quality indicator voters, to define which multi-objective evolutionary algorithm (MOEA) should generate more new solutions along the execution.
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Li, Ya-zhou, Jin Wang, Li-qin Hu, and Yi-can Wu. "A Variable Ordering Heuristic Based on Zero-Suppressed Binary Decision Diagrams." In 18th International Conference on Nuclear Engineering. ASMEDC, 2010. http://dx.doi.org/10.1115/icone18-30070.

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Two approaches have been proposed to solve the large-scale fault trees or event trees for Probabilistic Safety Assessment in a nuclear power plant. The first one consists in MCS/ZBDD, which uses ZBDDs (Zero-suppressed Binary Decision Diagrams) to implement classical MCS (Minimal Cut Sets) algorithm. The second consists in designing heuristics and strategies to reduce the complexity of the BDDs (Binary Decision Diagrams) construction. This paper was motivated to combine the MCS/ZBDD and designing heuristics for ZBDDs together. A heuristic, which took the failure rate of basic event into account and utilized that truncation could be implemented on ZBDDs during the calculating process, was proposed. This heuristic accelerated the analysis progress by bringing forward the truncation and reducing the complexity of the intermediate ZBDDs. RiskA, a Zero-suppressed Binary Decision Diagram package extended to safety and reliability analysis, has adopted this heuristic. RiskA’s truncation strategies, which had some relations with the ordering scheme, were also introduced. The correctness and efficiency of this new heuristic were verified by some practical models’ analyses.

Reports on the topic "Heuristic":

1

Feigenbaum, Edward A., and Bruce G. Buchanan. Heuristic Programming Project. Fort Belvoir, VA: Defense Technical Information Center, March 1986. http://dx.doi.org/10.21236/ada165995.

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Carter, Lynn. Probability driven heuristic nets. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.1998.

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Evett, Matthew, James Hendler, Ambuj Mahanti, and Dana Nau. PRA: Massively Parallel Heuristic Search. Fort Belvoir, VA: Defense Technical Information Center, January 1991. http://dx.doi.org/10.21236/ada454848.

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Hirshleifer, David, Yaron Levi, Ben Lourie, and Siew Hong Teoh. Decision Fatigue and Heuristic Analyst Forecasts. Cambridge, MA: National Bureau of Economic Research, February 2018. http://dx.doi.org/10.3386/w24293.

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Skiena, Steven S. Heuristic Approaches to Optimization with Applications. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada390374.

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Skiena, Steven S. Heuristic Approaches to Optimization With Applications. Fort Belvoir, VA: Defense Technical Information Center, August 2000. http://dx.doi.org/10.21236/ada382413.

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Nemhauser, George L., and Martin W. Savelsbergh. Combining Exact and Heuristic Approaches for Discrete Optimization. Fort Belvoir, VA: Defense Technical Information Center, February 2009. http://dx.doi.org/10.21236/ada495432.

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Kaku, Bharat K., Thomas E. Morton, and Gerald L. Thompson. A Heuristic Algorithm for the Facilities Layout Problem. Fort Belvoir, VA: Defense Technical Information Center, May 1988. http://dx.doi.org/10.21236/ada196093.

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Nemhauser, George L. Combining Exact ad Heuristic Approaches for Discrete Optimization. Fort Belvoir, VA: Defense Technical Information Center, December 2011. http://dx.doi.org/10.21236/ada567596.

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Martínez Martínez, Luis Enrique, and Juan M. Monserrat Gauchi. Heuristic Evaluation of Optical and Optometry Franchise Websites. Revista Latina de Comunicación Social, 2010. http://dx.doi.org/10.4185/rlcs-65-2010-884-071-088-en.

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