Gotowe bibliografie tematyczne / Heuristic programming

Gotowa bibliografia na temat „Heuristic programming”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 28 lipca 2024

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Artykuły w czasopismach na temat "Heuristic programming"

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MALITSKY, YURI, i MEINOLF SELLMANN. "STOCHASTIC OFFLINE PROGRAMMING". International Journal on Artificial Intelligence Tools 19, nr 04 (sierpień 2010): 351–71. http://dx.doi.org/10.1142/s0218213010000236.

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We propose a framework which we call stochastic off-line programming (SOP). The idea is to embed the development of combinatorial algorithms in an off-line learning environment which helps the developer choose heuristic advisors that guide the search for satisfying or optimal solutions. In particular, we consider the case where the developer has several heuristic advisors available. Rather than selecting a single heuristic, we propose that one of the heuristics is chosen randomly whenever the heuristic guidance is sought. The task of the SOP is to learn favorable instance-specific distributions of the heuristic advisors in order to boost the average-case performance of the resulting combinatorial algorithm. Applying this methodology to a typical optimization problem, we show that substantial improvements can in fact be achieved when we perform learning in an instances specific manner.
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WHITE, DOUGLAS J. "Heuristic Programming". IMA Journal of Management Mathematics 2, nr 2 (1989): 173–88. http://dx.doi.org/10.1093/imaman/2.2.173.

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Drake, John H., Matthew Hyde, Khaled Ibrahim i Ender Ozcan. "A genetic programming hyper-heuristic for the multidimensional knapsack problem". Kybernetes 43, nr 9/10 (3.11.2014): 1500–1511. http://dx.doi.org/10.1108/k-09-2013-0201.

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Purpose – Hyper-heuristics are a class of high-level search techniques which operate on a search space of heuristics rather than directly on a search space of solutions. The purpose of this paper is to investigate the suitability of using genetic programming as a hyper-heuristic methodology to generate constructive heuristics to solve the multidimensional 0-1 knapsack problem Design/methodology/approach – Early hyper-heuristics focused on selecting and applying a low-level heuristic at each stage of a search. Recent trends in hyper-heuristic research have led to a number of approaches being developed to automatically generate new heuristics from a set of heuristic components. A population of heuristics to rank knapsack items are trained on a subset of test problems and then applied to unseen instances. Findings – The results over a set of standard benchmarks show that genetic programming can be used to generate constructive heuristics which yield human-competitive results. Originality/value – In this work the authors show that genetic programming is suitable as a method to generate reusable constructive heuristics for the multidimensional 0-1 knapsack problem. This is classified as a hyper-heuristic approach as it operates on a search space of heuristics rather than a search space of solutions. To our knowledge, this is the first time in the literature a GP hyper-heuristic has been used to solve the multidimensional 0-1 knapsack problem. The results suggest that using GP to evolve ranking mechanisms merits further future research effort.
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Gebser, Martin, Benjamin Kaufmann, Javier Romero, Ramón Otero, Torsten Schaub i Philipp Wanko. "Domain-Specific Heuristics in Answer Set Programming". Proceedings of the AAAI Conference on Artificial Intelligence 27, nr 1 (30.06.2013): 350–56. http://dx.doi.org/10.1609/aaai.v27i1.8585.

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We introduce a general declarative framework for incorporating domain-specific heuristics into ASP solving. We accomplish this by extending the first-order modeling language of ASP by a distinguished heuristic predicate. The resulting heuristic information is processed as an equitable part of the logic program and subsequently exploited by the solver when it comes to non-deterministically assigning a truth value to an atom. We implemented our approach as a dedicated heuristic in the ASP solver clasp and show its great prospect by an empirical evaluation.
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Burke, Edmund K., Matthew R. Hyde, Graham Kendall i John Woodward. "Automating the Packing Heuristic Design Process with Genetic Programming". Evolutionary Computation 20, nr 1 (marzec 2012): 63–89. http://dx.doi.org/10.1162/evco_a_00044.

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The literature shows that one-, two-, and three-dimensional bin packing and knapsack packing are difficult problems in operational research. Many techniques, including exact, heuristic, and metaheuristic approaches, have been investigated to solve these problems and it is often not clear which method to use when presented with a new instance. This paper presents an approach which is motivated by the goal of building computer systems which can design heuristic methods. The overall aim is to explore the possibilities for automating the heuristic design process. We present a genetic programming system to automatically generate a good quality heuristic for each instance. It is not necessary to change the methodology depending on the problem type (one-, two-, or three-dimensional knapsack and bin packing problems), and it therefore has a level of generality unmatched by other systems in the literature. We carry out an extensive suite of experiments and compare with the best human designed heuristics in the literature. Note that our heuristic design methodology uses the same parameters for all the experiments. The contribution of this paper is to present a more general packing methodology than those currently available, and to show that, by using this methodology, it is possible for a computer system to design heuristics which are competitive with the human designed heuristics from the literature. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains.
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Soysal, Mehmet, Mustafa Çimen, Mine Ömürgönülşen i Sedat Belbağ. "Performance Comparison of Two Recent Heuristics for Green Time Dependent Vehicle Routing Problem". International Journal of Business Analytics 6, nr 4 (październik 2019): 1–11. http://dx.doi.org/10.4018/ijban.2019100101.

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This article concerns a green Time Dependent Capacitated Vehicle Routing Problem (TDCVRP) which is confronted in urban distribution planning. The problem is formulated as a Markovian Decision Process and a dynamic programming (DP) approach has been used for solving the problem. The article presents a performance comparison of two recent heuristics for the green TDCVRP that explicitly accounts for time dependent vehicle speeds and fuel consumption (emissions). These heuristics are the classical Restricted Dynamic Programming (RDP) algorithm, and the Simulation Based RDP that consists of weighted random sampling, RDP heuristic and simulation. The numerical experiments show that the Simulation Based Restricted Dynamic Programming heuristic can provide promising results within relatively short computational times compared to the classical Restricted Dynamic Programming for the green TDCVRP.
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Boston, Kevin, i Pete Bettinger. "An Analysis of Monte Carlo Integer Programming, Simulated Annealing, and Tabu Search Heuristics for Solving Spatial Harvest Scheduling Problems". Forest Science 45, nr 2 (1.05.1999): 292–301. http://dx.doi.org/10.1093/forestscience/45.2.292.

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Abstract Heuristics are commonly used to solve spatial harvest scheduling problems. They can generate spatially and temporally feasible solutions to large problems that traditional mathematical programming techniques are unable to solve. A common complaint about heuristics is that the quality of the solutions is unknown. We compared three heuristic techniques commonly used to solve spatial harvest scheduling problems: Monte Carlo integer programming, simulated annealing, and tabu search. Five hundred solutions to four problems, which had between 3000 to 5000 0-1 integer variables, were generated with each heuristic technique. In addition to the heuristic solutions, the optimal solution value was found to each problem using integer programming. Simulated annealing found the highest solution value for three of the four planning problems, and was less than 1% from the highest objective function value in the fourth problem. Tabu search located the best solution for the fourth planning problem. Monte Carlo integer programming had the lowest objective function for all four problems. Tabu search had the smallest range of solutions, followed by simulated annealing. Monte Carlo integer programming had the largest range of solutions. Using the Anderson-Darling statistics, the hypothesis that the solutions from each heuristic technique were distributed as a Weibull distribution was rejected for 10 of the 12 set of values. For the two solutions where the Weibull distribution was not rejected, the estimated optimal solution was found to be an unreliable estimate of the actual optimal solution. It appears that the reliability of using extreme value statistics to estimate the optimal solution is dependent on the quality of solutions generated by the heuristic procedure. For. Sci. 45(2):292-301.
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Ghaffariyan M, R., K. Stampfer, J. Sessions, T. Durston, CH Kanzian i M. Kuehmaier. "Road network optimization using heuristic and linear programming". Journal of Forest Science 56, No. 3 (1.04.2010): 137–45. http://dx.doi.org/10.17221/12/2009-jfs.

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&nbsp;To minimize the cost of logging, it is necessary to optimize the road density. The aim of this study was to determine optimal road spacing (ORS) in Northern Austria. The stepwise regression method was used in modelling. The production rate of tower yarder was 10.4 m<SUP>3</SUP>/PSHo (Productive system hours) and cost of 19.71 €.m<SUP>–3</SUP>. ORS was studied by calculating road construction cost, installation cost and yarding cost per m<SUP>3</SUP> for different road spacing. The minimum total cost occurred at 39.15 €.m<SUP>–3</SUP> and ORS would be 474 m assuming uphill and downhill yarding. The optimal road density and yarding distance are 21.1 m.ha<SUP>–1</SUP> and 90 m, respectively. A sample logging area was used to plan different roads and, using network analysis, the best solution was found based on a modified shortest path algorithm. The network analysis results were very different from the optimal road spacing results that assumed roads and logging corridors could be located anywhere in the planning area at a constant cost. Mixed integer programming was also used to get a real optimal solution.
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Pommerening, Florian, Gabriele Röger, Malte Helmert i Blai Bonet. "LP-Based Heuristics for Cost-Optimal Planning". Proceedings of the International Conference on Automated Planning and Scheduling 24 (11.05.2014): 226–34. http://dx.doi.org/10.1609/icaps.v24i1.13621.

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Many heuristics for cost-optimal planning are based on linear programming. We cover several interesting heuristics of this type by a common framework that fixes the objective function of the linear program. Within the framework, constraints from different heuristics can be combined in one heuristic estimate which dominates the maximum of the component heuristics. Different heuristics of the framework can be compared on the basis of their constraints. With this new method of analysis, we show dominance of the recent LP-based state-equation heuristic over optimal cost partitioning on single-variable abstractions. We also show that the previously suggested extension of the state-equation heuristic to exploit safe variables cannot lead to an improved heuristic estimate. We experimentally evaluate the potential of the proposed framework on an extensive suite of benchmark tasks.
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Joshi, Vijay, i Prasad Modak. "Heuristic Algorithms for Waste Load Allocation in a River Basin". Water Science and Technology 21, nr 8-9 (1.08.1989): 1057–64. http://dx.doi.org/10.2166/wst.1989.0307.

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Waste load allocation for rivers has been a topic of growing interest. Dynamic programming based algorithms are particularly attractive in this context and are widely reported in the literature. Codes developed for dynamic programming are however complex, require substantial computer resources and importantly do not allow interactions of the user. Further, there is always resistance to utilizing mathematical programming based algorithms for practical applications. There has been therefore always a gap between theory and practice in systems analysis in water quality management. This paper presents various heuristic algorithms to bridge this gap with supporting comparisons with dynamic programming based algorithms. These heuristics make a good use of the insight gained in the system's behaviour through experience, a process akin to the one adopted by field personnel and therefore can readily be understood by a user familiar with the system. Also they allow user preferences in decision making via on-line interaction. Experience has shown that these heuristics are indeed well founded and compare very favourably with the sophisticated dynamic programming algorithms. Two examples have been included which demonstrate such a success of the heuristic algorithms.
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Rozprawy doktorskie na temat "Heuristic programming"

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

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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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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)
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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)
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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)
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Burfoot, Daniel. "Limitations of and extensions to heuristic search planning". Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=100779.

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This thesis explores limitations of heuristic search planning, and presents techniques to overcome those limitations. The two halves of the thesis discuss problems in standard propositional planning (STRIPS) and in planning with numeric state variables respectively.
In the context of STRIPS, the primary focus is on the widely used relaxed plan heuristic (h+). A variety of cases are shown in which h+ provides systematically bad estimates of goal distance. To address this breakdown, a planning system called RRT-Plan is presented. This system is inspired by the concept of Rapidly-exploring Random Trees, which was originally developed for use in mobile robot path planning. Experimental results show that RRT-Plan is comparable to leading planners in terms of number of problems solved and plan quality. We conclude that the effectiveness of RRT-Plan is based on its ability to search the space of artificial goal orderings.
The second half of the work considers heuristic search planning in numeric domains. Two particularly significant obstacles are identified. The Curse of Affluence is due to the vast blowup in the search space caused by the addition of numeric variables. The Curse of Poverty relates to the difficulty of finding relevant lower bounds on resource consumption.
Exploration of the Curse of Affluence leads to the new concepts of reduced search and enhanced states. In reduced search, certain simple operators are not used to expand states. Instead, enhanced states are constructed which represent all possible states which could be achieved by suitably inserting simple operators in the plan. Enhanced states are represented by a set of constant discrete variables, and a convex hull of numeric values. This representation can be queried and updated in a natural way. Experimental results show that there are domains for which reduced search gives order of magnitude performance improvements over Metric-FF, a leading heuristic search planner for numeric domains.
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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.

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Hyde, Matthew. "A genetic programming hyper-heuristic approach to automated packing". Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/11625/.

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This thesis presents a programme of research which investigated a genetic programming hyper-heuristic methodology to automate the heuristic design process for one, two and three dimensional packing problems. Traditionally, heuristic search methodologies operate on a space of potential solutions to a problem. In contrast, a hyper-heuristic is a heuristic which searches a space of heuristics, rather than a solution space directly. The majority of hyper-heuristic research papers, so far, have involved selecting a heuristic, or sequence of heuristics, from a set pre-defined by the practitioner. Less well studied are hyper-heuristics which can create new heuristics, from a set of potential components. This thesis presents a genetic programming hyper-heuristic which makes it possible to automatically generate heuristics for a wide variety of packing problems. The genetic programming algorithm creates heuristics by intelligently combining components. The evolved heuristics are shown to be highly competitive with human created heuristics. The methodology is first applied to one dimensional bin packing, where the evolved heuristics are analysed to determine their quality, specialisation, robustness, and scalability. Importantly, it is shown that these heuristics are able to be reused on unseen problems. The methodology is then applied to the two dimensional packing problem to determine if automatic heuristic generation is possible for this domain. The three dimensional bin packing and knapsack problems are then addressed. It is shown that the genetic programming hyper-heuristic methodology can evolve human competitive heuristics, for the one, two, and three dimensional cases of both of these problems. No change of parameters or code is required between runs. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains.
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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/.

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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.
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Tian, Zhong Huan. "Gender based meta-heuristic optimization algorithms". Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691331.

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Sariklis, Dimitrios. "Open Vehicle Routing Problem : description, formulations and heuristic methods". Thesis, London School of Economics and Political Science (University of London), 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.265252.

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Książki na temat "Heuristic programming"

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

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J, Rayward-Smith V., red. Modern heuristic search methods. Chichester: Wiley, 1996.

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Ivanov, L. V. Analiz strategicheskikh resheniĭ (ėvristika). Moskva: RIOR, 2010.

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R, Reeves Colin, red. Modern heuristic techniques for combinatorial problems. London: McGraw-Hill, 1995.

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Shing, Man-Tak. A note on the maximum size of a rectilinear maze. Monterey, California: Naval Postgraduate School, 1989.

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Linköping, Universitetet i., red. Heuristics for minimum decompositions of polygons. Linköping, Sweden: Linköping University, Dept. of Computer and Information Science, 1987.

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L, Levy David N., i Beal D. F. 1948-, red. Heuristic programming in artificial intelligence: The first computer olympiad. Chichester: Ellis Horwood, 1989.

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L, Levy David N., i Beal D. F. 1948-, red. Heuristic programming in artificial intelligence: The second computer olympiad. New York: E. Horwood, 1991.

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R, Reeves Colin, red. Modern heuristic techniques for combinatorial problems. New York: Halsted Press, 1993.

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Eugene, Davis, Bresina John i Ames Research Center. Artificial Intelligence Research Branch., red. Learning to improve iterative repair scheduling. [Moffett Field, Calif.]: NASA, Ames Research Center, Artificial Intelligence Research Branch, 1992.

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Części książek na temat "Heuristic programming"

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Nakamura, K. "Heuristic prolog: Logic program execution by heuristic search". W Logic Programming '85, 148–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/3-540-16479-0_15.

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Eiselt, H. A., i C. L. Sandblom. "Heuristic Algorithms". W Integer Programming and Network Models, 229–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04197-0_11.

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Solnon, Christine, i Narendra Jussien. "Perturbative Heuristic Approaches". W Ant Colony Optimization and Constraint Programming, 69–84. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118557563.ch5.

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Solnon, Christine, i Narendra Jussien. "Constructive Heuristic Approaches". W Ant Colony Optimization and Constraint Programming, 85–92. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118557563.ch6.

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Foong, Wai Keong. "Or-parallel Prolog with heuristic task distribution". W Logic Programming, 193–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55460-2_14.

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Zipp, Jan Sebastian. "3 METHODOLOGY - HEURISTIC TENDENCIES WITHIN THE DISSERTATION". W Programming Creativity, 35–42. Bielefeld, Germany: transcript Verlag, 2022. http://dx.doi.org/10.14361/9783839463161-004.

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Keßler, C. W., W. J. Paul i T. Rauber. "A randomized heuristic approach to register allocation". W Programming Language Implementation and Logic Programming, 195–206. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/3-540-54444-5_99.

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Mandow, L., i E. Milián. "Goal Programming and Heuristic Search". W Lecture Notes in Economics and Mathematical Systems, 48–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-46854-4_5.

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Yamamoto, Yoshitaka, Katsumi Inoue i Koji Iwanuma. "Heuristic Inverse Subsumption in Full-Clausal Theories". W Inductive Logic Programming, 241–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38812-5_17.

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Zinn, Claus. "Heuristic Search over Program Transformations". W Declarative Programming and Knowledge Management, 234–49. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08909-6_15.

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Streszczenia konferencji na temat "Heuristic programming"

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Sadowski, Caitlin, i Sri Kurniawan. "Heuristic evaluation of programming language features". W the 3rd ACM SIGPLAN workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2089155.2089160.

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Solomonoff, Ray J. "Algorithmic Probability, Heuristic Programming and AGI". W 3d Conference on Artificial General Intelligence (AGI-10). Paris, France: Atlantis Press, 2010. http://dx.doi.org/10.2991/agi.2010.13.

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Zhi-Ming Han, Xian-Ping Liu i Miao Tang. "Heuristic search strategy of evolutionary programming". W 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010). IEEE, 2010. http://dx.doi.org/10.1109/car.2010.5456858.

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Burke, Edmund K., Matthew R. Hyde, Graham Kendall i John Woodward. "Automatic heuristic generation with genetic programming". W the 9th annual conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1276958.1277273.

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Ortiz-Bayliss, Jose Carlos, Ender Ozcan, Andrew J. Parkes i Hugo Terashima-Marin. "A genetic programming hyper-heuristic: Turning features into heuristics for constraint satisfaction". W 2013 13th UK Workshop on Computational Intelligence (UKCI). IEEE, 2013. http://dx.doi.org/10.1109/ukci.2013.6651304.

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Cohen, Liron, Tansel Uras, Shiva Jahangiri, Aliyah Arunasalam, Sven Koenig i T. K. Satish Kumar. "The FastMap Algorithm for Shortest Path Computations". W 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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Streszczenie:
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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Khalil, Elias B., Bistra Dilkina, George L. Nemhauser, Shabbir Ahmed i Yufen Shao. "Learning to Run Heuristics in Tree Search". W Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/92.

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``Primal heuristics'' are a key contributor to the improved performance of exact branch-and-bound solvers for combinatorial optimization and integer programming. Perhaps the most crucial question concerning primal heuristics is that of at which nodes they should run, to which the typical answer is via hard-coded rules or fixed solver parameters tuned, offline, by trial-and-error. Alternatively, a heuristic should be run when it is most likely to succeed, based on the problem instance's characteristics, the state of the search, etc. In this work, we study the problem of deciding at which node a heuristic should be run, such that the overall (primal) performance of the solver is optimized. To our knowledge, this is the first attempt at formalizing and systematically addressing this problem. Central to our approach is the use of Machine Learning (ML) for predicting whether a heuristic will succeed at a given node. We give a theoretical framework for analyzing this decision-making process in a simplified setting, propose a ML approach for modeling heuristic success likelihood, and design practical rules that leverage the ML models to dynamically decide whether to run a heuristic at each node of the search tree. Experimentally, our approach improves the primal performance of a state-of-the-art Mixed Integer Programming solver by up to 6% on a set of benchmark instances, and by up to 60% on a family of hard Independent Set instances.
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Siddiqui, F., i K. M. Iftekharuddin. "Multiresolution Object Recognition Using Dual Heuristic Programming". W The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.246999.

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Sun, Jian, Feng Liu, Jennie Si i Shengwei Mei. "Direct heuristic dynamic programming with augmented states". W 2011 International Joint Conference on Neural Networks (IJCNN 2011 - San Jose). IEEE, 2011. http://dx.doi.org/10.1109/ijcnn.2011.6033633.

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Tao Li, Dongbin Zhao i Jianqiang Yi. "Heuristic Dynamic Programming strategy with eligibility traces". W 2008 American Control Conference (ACC '08). IEEE, 2008. http://dx.doi.org/10.1109/acc.2008.4587210.

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Raporty organizacyjne na temat "Heuristic programming"

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

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Saltzman, Robert M. A Heuristic Ceiling Point Algorithm for General Integer Linear Programming. Fort Belvoir, VA: Defense Technical Information Center, listopad 1988. http://dx.doi.org/10.21236/ada202285.

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Sterns, Anthony, Ronni Sterns, Jeffrey Adler, Douglas Kline i Scott Collins. The Neighborhood Covering Heuristic (NCH) Approach for the General Mixed Integer Programming Problem. Fort Belvoir, VA: Defense Technical Information Center, luty 2004. http://dx.doi.org/10.21236/ada421653.

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Harris, Sean. A Comparison of Genetic Programming Variants for Hyper-Heuristics. Office of Scientific and Technical Information (OSTI), marzec 2015. http://dx.doi.org/10.2172/1177599.

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