A comparison of Linkage-learning-based genetic algorithms in multidimensional knapsack problems

2013 IEEE Congress on Evolutionary Computation, CEC 2013

Volumn , Issue , 2013, Pages 502-509

  Martins, Jean P ^a   Neto, Constancio Bringel ^b   Crocomo, Marcio K ^a   Vittori, Karla ^b   Delbem, Alexandre C B ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

^b UNIVERSIDADE FEDERAL DO ABC   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN OPTIMIZATION ALGORITHMS; COMMUNITY DETECTION; COMPACT GENETIC ALGORITHM; COMPLEX OPTIMIZATION PROBLEMS; GENETIC ALGORITHM (GAS); MULTIDIMENSIONAL KNAPSACK PROBLEMS; OPTIMIZATION PROBLEMS; RELATIVE PERFORMANCE;

COMBINATORIAL OPTIMIZATION; OPTIMIZATION; POPULATION STATISTICS; PROBLEM SOLVING; TREES (MATHEMATICS);

GENETIC ALGORITHMS;

EID: 84881588184     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2013.6557610     Document Type: Conference Paper

References (31)

1. H. Simon, The sciences of the artificial. The MIT Press, 1996.
2. J. H. Holland, Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, 1975.
3. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional, January 1989.
4. D. Goldberg, The design of innovation: Lessons from and for competent genetic algorithms. Springer, 2002.
5. G. Harik and D. Goldberg, "Learning linkage," in Foundations of Genetic Algorithms, R. K. Belew and M. D. Vose, Eds., vol. 4. Morgan Kaufmann Publishers, 1997, pp. 247-262.
6. G. R. Harik, F. G. Lobo, and K. Sastry, "Linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm (ECGA)," in Scalable Optimization via Probabilistic Modeling. Springer, 2006, pp. 39-61.
7. M. Pelikan, D. E. Goldberg, and E. Cant́u-Paz, "BOA: The Bayesian Optimization Algorithm," in Proceedings of the Genetic and Evolutionary Computation Conference GECCO-99, vol. I. Orlando, FL: Morgan Kaufmann Publishers, San Fransisco, CA, 1999, pp. 525-532.
8. M. Pelikan and D. Goldberg, "Hierarchical BOA solves Ising spin glasses and MAXSAT," in Genetic and Evolutionary Computation-GECCO 2003. Springer, 2003, pp. 213-213.
9. M. Pelikan, Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms, 1st ed., ser. Studies in Fuzziness and Soft Computing. Springer, March 2005.
10. -, "NK landscapes, problem difficulty, and hybrid evolutionary algorithms," in Proceedings of the 12th annual conference on Genetic and evolutionary computation, ser. GECCO '10. New York, NY, USA: ACM, 2010, pp. 665-672. [Online]. Available: http://doi.acm.org/10.1145/1830483.1830606
11. D. Thierens, "The Linkage Tree Genetic Algorithm," in Parallel Problem Solving from Nature, PPSN XI, ser. Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2010, vol. 6238, pp. 264-273.
12. M. Pelikan, M. W. Hauschild, and D. Thierens, "Pairwise and problem-specific distance metrics in the linkage tree genetic algorithm," in Proceedings of the 13th annual conference on Genetic and evolutionary computation, ser. GECCO '11. New York, NY, USA: ACM, 2011, pp. 1005-1012. [Online]. Available: http://doi.acm.org/10.1145/2001576.2001713
13. B. Goldman and D. Tauritz, "Linkage tree genetic algorithms: variants and analysis," in Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference. ACM, 2012, pp. 625-632.
14. M. Hauschild, M. Pelikan, C. F. Lima, and K. Sastry, "Analyzing probabilistic models in hierarchical BOA on traps and spin glasses," in Proceedings of the 9th annual conference on Genetic and evolutionary computation, ser. GECCO '07. New York, NY, USA: ACM, 2007, pp. 523-530. [Online]. Available: http://doi.acm.org/10.1145/1276958. 1277070
15. A. Fŕeville, "The multidimensional 0-1 knapsack problem: An overview," European Journal of Operational Research, vol. 155, no. 1, pp. 1-21, 2004. [Online]. Available: http://www.sciencedirect.com/science/article/ pii/S0377221703002741
16. H. Kellerer, U. Pferschy, and D. Pisinger, Knapsack problems. Springer Verlag, 2004.
17. A. Kulik and H. Shachnai, "There is no EPTAS for two-dimensional knapsack," Information Processing Letters, vol. 110, no. 16, pp. 707-710, 2010. [Online]. Available: http://www.sciencedirect.com/science/article/pii/ S0020019010001626
18. S. Martello and P. Toth, Knapsack problems: algorithms and computer implementations. New York, NY, USA: John Wiley & Sons, Inc., 1990.
19. P. Chu and J. Beasley, "A Genetic Algorithm for the Multidimensional Knapsack Problem," Journal of Heuristics, vol. 4, pp. 63-86, 1998. [Online]. Available: http://dx.doi.org/10.1023/A:1009642405419
20. J. Gottlieb, "On the Feasibility Problem of Penalty-Based Evolutionary Algorithms for Knapsack Problems," in Applications of Evolutionary Computing, ser. Lecture Notes in Computer Science, E. Boers, Ed. Springer Berlin Heidelberg, 2001, vol. 2037, pp. 50-59. [Online]. Available: http://dx.doi.org/10.1007/3-540-45365-2 6
21. J. Tavares, F. Pereira, and E. Costa, "The Role of Representation on the Multidimensional Knapsack Problem by means of Fitness Landscape Analysis," in Evolutionary Computation, 2006. CEC 2006. IEEE Congress on, 0-0 2006, pp. 2307-2314.
22. "Multidimensional Knapsack Problem: A Fitness Landscape Analysis," Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 38, no. 3, pp. 604-616, june 2008.
23. G. Harik, "Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms," Ph.D. dissertation, The University of Michigan, 1997.
24. M. Pelikan, D. E. Goldberg, K. Sastry et al., "Bayesian optimization algorithm, decision graphs, and Occam's razor," in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), 2001, pp. 519-526.
25. M. Pelikan, K. Sastry, and E. Cantu-Paz, Scalable optimization via probabilistic modeling: From algorithms to applications. Springer Verlag, 2006.
26. M. E. J. Newman and M. Girvan, "Finding and evaluating community structure in networks," Physical Review E, vol. 69, no. 2, pp. 026 113+, Feb 2004. [Online]. Available: http://dx.doi.org/10.1103/PhysRevE.69. 026113
27. M. K. Crocomo, "Algoritmo de otimizaç ̃ao bayesiano com detecç ̃ao de comunidades," Ph.D. dissertation, Universidade de S̃ao Paulo, S̃ao Carlos, 2012.
28. D. Thierens and P. A. Bosman, "Optimal mixing evolutionary algorithms," in Proceedings of the 13th annual conference on Genetic and evolutionary computation, ser. GECCO '11. New York, NY, USA: ACM, 2011, pp. 617-624.
29. W. Day and H. Edelsbrunner, "Efficient algorithms for agglomerative hierarchical clustering methods," Journal of classification, vol. 1, no. 1, pp. 7-24, 1984.
30. T. Cover, J. Thomas, J. Wiley et al., Elements of information theory. Wiley Online Library, 1991, vol. 6.
31. I. Gronau and S. Moran, "Optimal implementations of UPGMA and other common clustering algorithms," Information Processing Letters, vol. 104, no. 6, pp. 205-210, 2007. [Online]. Available: http://www.sciencedirect.com/ science/article/pii/S0020019007001767