The influence of linkage-learning in the linkage-tree GA when solving multidimensional knapsack problems

GECCO 2013 - Proceedings of the 2013 Genetic and Evolutionary Computation Conference

Volumn , Issue , 2013, Pages 821-828

  Martins, Jean P ^a   Delbem, Alexandre C B ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

Linkage learning; Linkage tree GA; Silhouette

Indexed keywords

BLACK-BOX OPTIMIZATION; DEPENDENT VARIABLES; LINKAGE-LEARNING; MULTIDIMENSIONAL KNAPSACK PROBLEMS; NEARLY DECOMPOSABLE; OPTIMIZATION PROBLEMS; SILHOUETTE; TRAP FUNCTIONS;

COMBINATORIAL OPTIMIZATION; FORESTRY; OPTIMIZATION;

GENETIC ALGORITHMS;

ALGORITHMS; FORESTRY; OPTIMIZATION;

EID: 84883076824     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2463372.2463476     Document Type: Conference Paper

References (26)

1. A. Barron, J. Rissanen, and B. Yu. The Minimum Description Length Principle in Coding and Modeling. Information Theory, IEEE Transactions on, 44(6):2743-2760, oct 1998.
2. P. Chu and J. Beasley. A Genetic Algorithm for the Multidimensional Knapsack Problem. Journal of Heuristics, 4:63-86, 1998.
3. T. Cover, J. Thomas, J. Wiley, et al. Elements of information theory, volume 6. Wiley Online Library, 1991.
4. A. Fréville. The multidimensional 0-1 knapsack problem: An overview. European Journal of Operational Research, 155(1):1-21, 2004.
5. D. E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional, January 1989.
6. 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, pages 625-632. ACM, 2012.
7. J. Gottlieb. On the Feasibility Problem of Penalty-Based Evolutionary Algorithms for Knapsack Problems. In E. Boers, editor, Applications of Evolutionary Computing, volume 2037 of Lecture Notes in Computer Science, pages 50-59. Springer Berlin Heidelberg, 2001.
8. I. Gronau and S. Moran. Optimal implementations of UPGMA and other common clustering algorithms. Information Processing Letters, 104(6):205-210, 2007.
9. G. Harik. Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms. PhD thesis, The University of Michigan, 1997.
10. J. H. Holland. Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, 1975.
11. A. Kraskov, H. Stogbauer, R. Andrzejak, and P. Grassberger. Hierarchical clustering using mutual information. EPL (Europhysics Letters), 70(2), 2005.
12. A. Kulik and H. Shachnai. There is no EPTAS for two-dimensional knapsack. Information Processing Letters, 110(16):707-710, 2010.
13. S. Martello and P. Toth. Knapsack problems: algorithms and computer implementations. John Wiley & Sons, Inc., New York, NY, USA, 1990.
14. J. P. Martins, A. H. M. Soares, D. Vargas, and A. C. B. Delbem. Multi-objective Phylogenetic Algorithm: Solving Multi-objective Decomposable Deceptive Problems. In R. Takahashi, K. Deb, E. Wanner, and S. Greco, editors, Evolutionary Multi-Criterion Optimization, volume 6576 of Lecture Notes in Computer Science, pages 285-297. Springer Berlin/Heidelberg, 2011.
15. M. Pelikan. Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms. Studies in Fuzziness and Soft Computing. Springer, 1 edition, March 2005.
16. M. Pelikan. NK landscapes, problem difficulty, and hybrid evolutionary algorithms. In Proceedi ngs of the 12th annual conference on Genetic and evolutionary computation, GECCO '10, pages 665-672, New York, NY, USA, 2010. ACM.
17. 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, GECCO '11, pages 1005-1012, New York, NY, USA, 2011. ACM.
18. P. J. Rousseeuw. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20(0):53-65, 1987.
19. K. Sastry. Evaluation-relaxation schemes for genetic and evolutionary algorithms. PhD thesis, University of Illinois at Urbana-Champaign, 2001.
20. H. Simon. The sciences of the artificial. the MIT Press, 1996.
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, pages 2307-2314, 0-0 2006.
22. J. Tavares, F. Pereira, and E. Costa. Multidimensional Knapsack Problem: A Fitness Landscape Analysis. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 38(3):604-616, june 2008.
23. D. Thierens. The Linkage Tree Genetic Algorithm. In Parallel Problem Solving from Nature, PPSN XI, volume 6238 of Lecture Notes in Computer Science, pages 264-273. Springer Berlin Heidelberg, 2010.
24. D. Thierens and P. A. Bosman. Optimal mixing evolutionary algorithms. In Proceedings of the 1 3th annual conference on Genetic and evolutionary computation, GECCO '11, pages 617-624, New York, NY, USA, 2011. ACM.
25. V. Veloso de Melo, D. Vargas, and M. Crocomo. Phylogenetic Differential Evolution. International Journal of Natural Computing Research (IJNCR), 2(1):21-38, 2011.
26. D. Wolpert and W. Macready. No free lunch theorems for optimization. Evolutionary Computation, IEEE Transactions on, 1(1):67-82, 2002.