Abstract
We introduce the Linkage Tree Genetic Algorithm (LTGA), a competent genetic algorithm that learns the linkage between the problem variables. The LTGA builds each generation a linkage tree using a hierarchical clustering algorithm. To generate new offspring solutions, the LTGA selects two parent solutions and traverses the linkage tree starting from the root. At each branching point, the parent pair is recombined using a crossover mask defined by the clustering at that particular tree node. The parent pair competes with the offspring pair, and the LTGA continues traversing the linkage tree with the pair that has the most fit solution. Once the entire tree is traversed, the best solution of the current pair is copied to the next generation. In this paper we use the normalized variation of information metric as distance measure for the clustering process. Experimental results for fully deceptive functions and nearest neighbor NK-landscape problems with tunable overlap show that the LTGA can solve these hard functions efficiently without knowing the actual position of the linked variables on the problem representation.
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References
Chen, Y.-P., Lim, M.-H. (eds.): Linkage in Evolutionary Computation. Studies in Computational Intelligence, vol. 157. Springer, Heidelberg (2008)
Deb, K., Goldberg, D.E.: Analyzing deception in trap functions. In: Whitley, L.D. (ed.) Proceedings of the Second Workshop on Foundations of Genetic Algorithms, pp. 93–108. Morgan Kaufmann, San Francisco (1993)
Duque, T.S., Goldberg, D.E.: A new method for linkage learning in the ECGA. In: Proceedings of the 11th annual conference on Genetic and Evolutionary Computation, pp. 1819–1820. ACM, New York (2009)
Goldberg, D.E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers, Dordrecht (2002)
Harik, G.R., Lobo, F.G., Sastry, K.: Linkage learning via probabilistic modeling in the extended compact genetic algorithm. In: Pelikan [9], pp. 39–61
Hauschild, M., Pelikan, M.: Network crossover performance on NK landscapes and deceptive problems. Technical Report MEDAL No. 2010003, Missouri Estimation of Distribution Algorithms Laboratory (January 2010)
Kraskov, A., Stögbauer, H., Andrzejak, R.G., Grassberger, P.: Hierarchical clustering using mutual information. EuroPhysics Letters 70(2), 278–284 (2005)
Pelikan, M., Sastry, K., Butz, M.V., Goldberg, D.E.: Performance of evolutionary algorithms on random decomposable problems. In: Runarsson, T.P., Beyer, H.-G., Burke, E.K., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds.) PPSN 2006. LNCS, vol. 4193, pp. 788–797. Springer, Heidelberg (2006)
Pelikan, M., Sastry, K., Cantú-Paz, E. (eds.): Scalable Optimization via Probabilistic Modeling. Studies in Computational Intelligence, vol. 33. Springer, Heidelberg (2006)
Pelikan, M., Sastry, K., Goldberg, D.E., Butz, M.V., Hauschild, M.: Performance of evolutionary algorithms on NK landscapes with nearest neighbor interactions and tunable overlap. In: Raidl, G. (ed.) Proceedings of the 11th annual conference on Genetic and Evolutionary Computation, pp. 851–858. ACM, New York (2009)
Stonedahl, F., Rand, W., Wilensky, U.: Crossnet: a framework for crossover with network-based chromosomal representations. In: Proceedings of the 10th annual conference on Genetic and Evolutionary computation, pp. 1057–1064. ACM, New York (2008)
Thierens, D., Goldberg, D.E.: Mixing in genetic algorithms. In: Proceedings of the International Conference on Genetic Algorithms (ICGA), pp. 38–47 (1993)
Yu, T.-L., Goldberg, D.E.: Conquering hierarchical difficulty by explicit chunking: substructural chromosome compression. In: Cattolico, M. (ed.) Proceedings of the 8th annual conference on Genetic and Evolutionary computation, pp. 1385–1392. ACM, New York (2006)
Yu, T.-L., Sastry, K., Goldberg, D.E., Pelikan, M.: Population sizing for entropy-based model building in discrete estimation of distribution algorithms. In: Proceedings of the 9th annual conference on Genetic and Evolutionary Computation, pp. 601–608. ACM, New York (2007)
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Thierens, D. (2010). The Linkage Tree Genetic Algorithm. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15844-5_27
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DOI: https://doi.org/10.1007/978-3-642-15844-5_27
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