Academic literature on the topic 'Heuristic programming'
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Journal articles on the topic "Heuristic programming"
MALITSKY, YURI, and MEINOLF SELLMANN. "STOCHASTIC OFFLINE PROGRAMMING." International Journal on Artificial Intelligence Tools 19, no. 04 (August 2010): 351–71. http://dx.doi.org/10.1142/s0218213010000236.
Full textWHITE, DOUGLAS J. "Heuristic Programming." IMA Journal of Management Mathematics 2, no. 2 (1989): 173–88. http://dx.doi.org/10.1093/imaman/2.2.173.
Full textDrake, John H., Matthew Hyde, Khaled Ibrahim, and Ender Ozcan. "A genetic programming hyper-heuristic for the multidimensional knapsack problem." Kybernetes 43, no. 9/10 (November 3, 2014): 1500–1511. http://dx.doi.org/10.1108/k-09-2013-0201.
Full textGebser, Martin, Benjamin Kaufmann, Javier Romero, Ramón Otero, Torsten Schaub, and Philipp Wanko. "Domain-Specific Heuristics in Answer Set Programming." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 30, 2013): 350–56. http://dx.doi.org/10.1609/aaai.v27i1.8585.
Full textBurke, Edmund K., Matthew R. Hyde, Graham Kendall, and John Woodward. "Automating the Packing Heuristic Design Process with Genetic Programming." Evolutionary Computation 20, no. 1 (March 2012): 63–89. http://dx.doi.org/10.1162/evco_a_00044.
Full textSoysal, Mehmet, Mustafa Çimen, Mine Ömürgönülşen, and Sedat Belbağ. "Performance Comparison of Two Recent Heuristics for Green Time Dependent Vehicle Routing Problem." International Journal of Business Analytics 6, no. 4 (October 2019): 1–11. http://dx.doi.org/10.4018/ijban.2019100101.
Full textBoston, Kevin, and Pete Bettinger. "An Analysis of Monte Carlo Integer Programming, Simulated Annealing, and Tabu Search Heuristics for Solving Spatial Harvest Scheduling Problems." Forest Science 45, no. 2 (May 1, 1999): 292–301. http://dx.doi.org/10.1093/forestscience/45.2.292.
Full textGhaffariyan M, R., K. Stampfer, J. Sessions, T. Durston, CH Kanzian, and M. Kuehmaier. "Road network optimization using heuristic and linear programming." Journal of Forest Science 56, No. 3 (April 1, 2010): 137–45. http://dx.doi.org/10.17221/12/2009-jfs.
Full textPommerening, Florian, Gabriele Röger, Malte Helmert, and Blai Bonet. "LP-Based Heuristics for Cost-Optimal Planning." Proceedings of the International Conference on Automated Planning and Scheduling 24 (May 11, 2014): 226–34. http://dx.doi.org/10.1609/icaps.v24i1.13621.
Full textJoshi, Vijay, and Prasad Modak. "Heuristic Algorithms for Waste Load Allocation in a River Basin." Water Science and Technology 21, no. 8-9 (August 1, 1989): 1057–64. http://dx.doi.org/10.2166/wst.1989.0307.
Full textDissertations / Theses on the topic "Heuristic programming"
Ambrogi, Timothy. "Heuristic counterpoint." Diss., Connect to the thesis, 2004. http://hdl.handle.net/10066/1484.
Full textPerry, Kristine. "Heuristic weighted voting /." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2120.pdf.
Full textSilva, 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.
Full textCoordenação 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)
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.
Full textBanca: 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
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.
Full textIn 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.
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.
Full textHyde, Matthew. "A genetic programming hyper-heuristic approach to automated packing." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/11625/.
Full textHong, 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/.
Full textTian, Zhong Huan. "Gender based meta-heuristic optimization algorithms." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691331.
Full textSariklis, 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.
Full textBooks on the topic "Heuristic programming"
Clancey, William J. Heuristic classification. [Alexandria, Va.]: DTIC, 1985.
Find full textJ, Rayward-Smith V., ed. Modern heuristic search methods. Chichester: Wiley, 1996.
Find full textIvanov, L. V. Analiz strategicheskikh resheniĭ (ėvristika). Moskva: RIOR, 2010.
Find full textR, Reeves Colin, ed. Modern heuristic techniques for combinatorial problems. London: McGraw-Hill, 1995.
Find full textShing, Man-Tak. A note on the maximum size of a rectilinear maze. Monterey, California: Naval Postgraduate School, 1989.
Find full textLinköping, Universitetet i., ed. Heuristics for minimum decompositions of polygons. Linköping, Sweden: Linköping University, Dept. of Computer and Information Science, 1987.
Find full textL, Levy David N., and Beal D. F. 1948-, eds. Heuristic programming in artificial intelligence: The first computer olympiad. Chichester: Ellis Horwood, 1989.
Find full textL, Levy David N., and Beal D. F. 1948-, eds. Heuristic programming in artificial intelligence: The second computer olympiad. New York: E. Horwood, 1991.
Find full textR, Reeves Colin, ed. Modern heuristic techniques for combinatorial problems. New York: Halsted Press, 1993.
Find full textEugene, Davis, Bresina John, and Ames Research Center. Artificial Intelligence Research Branch., eds. Learning to improve iterative repair scheduling. [Moffett Field, Calif.]: NASA, Ames Research Center, Artificial Intelligence Research Branch, 1992.
Find full textBook chapters on the topic "Heuristic programming"
Nakamura, K. "Heuristic prolog: Logic program execution by heuristic search." In Logic Programming '85, 148–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/3-540-16479-0_15.
Full textEiselt, H. A., and C. L. Sandblom. "Heuristic Algorithms." In 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.
Full textSolnon, Christine, and Narendra Jussien. "Perturbative Heuristic Approaches." In Ant Colony Optimization and Constraint Programming, 69–84. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118557563.ch5.
Full textSolnon, Christine, and Narendra Jussien. "Constructive Heuristic Approaches." In Ant Colony Optimization and Constraint Programming, 85–92. Hoboken, NJ USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118557563.ch6.
Full textFoong, Wai Keong. "Or-parallel Prolog with heuristic task distribution." In Logic Programming, 193–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/3-540-55460-2_14.
Full textZipp, Jan Sebastian. "3 METHODOLOGY - HEURISTIC TENDENCIES WITHIN THE DISSERTATION." In Programming Creativity, 35–42. Bielefeld, Germany: transcript Verlag, 2022. http://dx.doi.org/10.14361/9783839463161-004.
Full textKeßler, C. W., W. J. Paul, and T. Rauber. "A randomized heuristic approach to register allocation." In 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.
Full textMandow, L., and E. Milián. "Goal Programming and Heuristic Search." In 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.
Full textYamamoto, Yoshitaka, Katsumi Inoue, and Koji Iwanuma. "Heuristic Inverse Subsumption in Full-Clausal Theories." In Inductive Logic Programming, 241–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38812-5_17.
Full textZinn, Claus. "Heuristic Search over Program Transformations." In Declarative Programming and Knowledge Management, 234–49. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08909-6_15.
Full textConference papers on the topic "Heuristic programming"
Sadowski, Caitlin, and Sri Kurniawan. "Heuristic evaluation of programming language features." In the 3rd ACM SIGPLAN workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2089155.2089160.
Full textSolomonoff, Ray J. "Algorithmic Probability, Heuristic Programming and AGI." In 3d Conference on Artificial General Intelligence (AGI-10). Paris, France: Atlantis Press, 2010. http://dx.doi.org/10.2991/agi.2010.13.
Full textZhi-Ming Han, Xian-Ping Liu, and Miao Tang. "Heuristic search strategy of evolutionary programming." In 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.
Full textBurke, Edmund K., Matthew R. Hyde, Graham Kendall, and John Woodward. "Automatic heuristic generation with genetic programming." In the 9th annual conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1276958.1277273.
Full textOrtiz-Bayliss, Jose Carlos, Ender Ozcan, Andrew J. Parkes, and Hugo Terashima-Marin. "A genetic programming hyper-heuristic: Turning features into heuristics for constraint satisfaction." In 2013 13th UK Workshop on Computational Intelligence (UKCI). IEEE, 2013. http://dx.doi.org/10.1109/ukci.2013.6651304.
Full textCohen, 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.
Full textKhalil, Elias B., Bistra Dilkina, George L. Nemhauser, Shabbir Ahmed, and Yufen Shao. "Learning to Run Heuristics in Tree Search." 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.2017/92.
Full textSiddiqui, F., and K. M. Iftekharuddin. "Multiresolution Object Recognition Using Dual Heuristic Programming." In The 2006 IEEE International Joint Conference on Neural Network Proceedings. IEEE, 2006. http://dx.doi.org/10.1109/ijcnn.2006.246999.
Full textSun, Jian, Feng Liu, Jennie Si, and Shengwei Mei. "Direct heuristic dynamic programming with augmented states." In 2011 International Joint Conference on Neural Networks (IJCNN 2011 - San Jose). IEEE, 2011. http://dx.doi.org/10.1109/ijcnn.2011.6033633.
Full textTao Li, Dongbin Zhao, and Jianqiang Yi. "Heuristic Dynamic Programming strategy with eligibility traces." In 2008 American Control Conference (ACC '08). IEEE, 2008. http://dx.doi.org/10.1109/acc.2008.4587210.
Full textReports on the topic "Heuristic programming"
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.
Full textSaltzman, Robert M. A Heuristic Ceiling Point Algorithm for General Integer Linear Programming. Fort Belvoir, VA: Defense Technical Information Center, November 1988. http://dx.doi.org/10.21236/ada202285.
Full textSterns, Anthony, Ronni Sterns, Jeffrey Adler, Douglas Kline, and Scott Collins. The Neighborhood Covering Heuristic (NCH) Approach for the General Mixed Integer Programming Problem. Fort Belvoir, VA: Defense Technical Information Center, February 2004. http://dx.doi.org/10.21236/ada421653.
Full textHarris, Sean. A Comparison of Genetic Programming Variants for Hyper-Heuristics. Office of Scientific and Technical Information (OSTI), March 2015. http://dx.doi.org/10.2172/1177599.
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