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Literatura académica sobre el tema "Heuristic"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 25 de julio de 2025

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Artículos de revistas sobre el tema "Heuristic"

1

Seipp, Jendrik. "Better Orders for Saturated Cost Partitioning in Optimal Classical Planning." Proceedings of the International Symposium on Combinatorial Search 8, no. 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

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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 (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 gene

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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

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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

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Shperberg, Shahaf, Ariel Felner, Lior Siag, and Nathan R. Sturtevant. "On the Properties of All-Pair Heuristics." Proceedings of the International Symposium on Combinatorial Search 17 (June 1, 2024): 127–33. http://dx.doi.org/10.1609/socs.v17i1.31550.

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While most work in heuristic search concentrates on goal-specific heuristics, which estimate the shortest path cost from any state to the goal, we explore all-pair heuristics that estimate distances between all pairs of states. We examine the relationship between these heuristic functions and the shortest distance function they estimate, revealing that all-pair consistent heuristics may violate the triangle inequality. Thus, we introduce a new property for heuristics called Δ-consistency, requiring adherence to the triangle inequality. Additionally, we present a method for transforming standar

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Chen, Dillon Z., and Sylvie Thiébaux. "Novelty Heuristics, Multi-Queue Search, and Portfolios for Numeric Planning." Proceedings of the International Symposium on Combinatorial Search 17 (June 1, 2024): 203–7. http://dx.doi.org/10.1609/socs.v17i1.31559.

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Heuristic search is a powerful approach for solving planning problems and numeric planning is no exception. In this paper, we boost the performance of heuristic search for numeric planning with various powerful techniques orthogonal to improving heuristic informedness: numeric novelty heuristics, the Manhattan distance heuristic, and exploring the use of multi-queue search and portfolios for combining heuristics.

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7

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 (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 wi

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8

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(

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BOUZY, BRUNO. "HISTORY AND TERRITORY HEURISTICS FOR MONTE CARLO GO." New Mathematics and Natural Computation 02, no. 02 (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 th

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10

Wilt, Christopher, and Wheeler Ruml. "Speedy Versus Greedy Search." Proceedings of the International Symposium on Combinatorial Search 5, no. 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 appea

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

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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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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<br>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

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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 res

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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)<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)<br>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 inclusã

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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<br>Banca: Marcos Antonio Pereira<br>Banca: Anibal Tavares de Azevedo<br>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 "no

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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

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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

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Libros sobre el tema "Heuristic"

1

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

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

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

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

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5

Clancey, William J. Heuristic classification. DTIC, 1985.

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Ings, Welby, and Keith Tudor. Heuristic Enquiries. Routledge, 2024. http://dx.doi.org/10.4324/9781003507758.

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

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Lappin, Gerald F. Heuristic symbolic integration. The Author], 1987.

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9

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

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

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Capítulos de libros sobre el tema "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. 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. 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. 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. 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. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-6000-2_311-1.

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Xiang, Yao, and Yan Guoli. "Heuristic." In The ECPH Encyclopedia of Psychology. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-7874-4_311.

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Salhi, Saïd. "Introduction." In Heuristic Search. 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. 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. 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. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49355-8_4.

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Actas de conferencias sobre el tema "Heuristic"

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Aoki, Yoichi, Keito Kudo, Tatsuki Kuribayashi, et al. "First Heuristic Then Rational: Dynamic Use of Heuristics in Language Model Reasoning." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-main.789.

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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 retu

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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 inter

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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}. 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 proble

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Núñez-Molina, Carlos, Masataro Asai, Pablo Mesejo, and Juan Fernandez-Olivares. "On Using Admissible Bounds for Learning Forward Search Heuristics." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/747.

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In recent years, there has been growing interest in utilizing modern machine learning techniques to learn heuristic functions for forward search algorithms. Despite this, there has been little theoretical understanding of what they should learn, how to train them, and why we do so. This lack of understanding has resulted in the adoption of diverse training targets (suboptimal vs optimal costs vs admissible heuristics) and loss functions (e.g., square vs absolute errors) in the literature. In this work, we focus on how to effectively utilize the information provided by admissible heuristics in

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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}. 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 ena

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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. 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 reachabl

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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 ado

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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}. 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

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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}. 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 appr

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Informes sobre el tema "Heuristic"

1

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

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2

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

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3

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

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4

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

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5

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

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6

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

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7

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

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8

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

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9

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

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10

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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