Abstract
For an evolutionary algorithm (EA) to be efficiently scalable, variation must be linkage friendly. For this reason many EAs have been introduced that build and exploit linkage models, amongst which are estimation-of-distribution algorithms (EDAs). Although various models have been empirically evaluated, it remains of key importance to better understand the conditions under which model building is successful. In this paper, we consider the linkage tree genetic algorithm (LTGA). LTGA is a recent powerful linkage-learning EA that builds a hierarchical linkage model known as the linkage tree (LT). LTGA exploits this model using an intensive mixing procedure aimed at optimally exchanging building blocks. Empirical evaluation studies of LTGA have appeared in literature using different entropy-based measures for building the LT, but with comparable results. We study the differences in these measures to better understand the requirements for detecting important linkage information and point out why some measures are more successful than others.
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Bosman, P.A.N., Thierens, D.: Linkage neighbors, optimal mixing and forced improvements in genetic algorithms. In: Proc. of the Genetic and Evolutionary Computation Conf., GECCO 2012. ACM Press, New York (to appear, 2012)
Deb, K., Goldberg, D.E.: Sufficient conditions for arbitrary binary functions. Annals of Mathematics and Artificial Intelligence 10(4), 385–408 (1994)
Harik, G.R., Lobo, F.G., Sastry, K.: Linkage learning via probabilistic modeling in the extended compact genetic algorithm (ECGA). In: Pelikan, M., et al. (eds.) Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications, pp. 39–61. Springer, Berlin (2006)
Kraskov, A., Grassberger, P.: MIC: Mutual information based hierarchical clustering. In: Emmert-Streib, F., Dehmer, M. (eds.) Knowledge Incorporation in Evolutionary Computation, pp. 101–123. Springer, Berlin (2009)
Pelikan, M., Hauschild, M.W., Thierens, D.: Pairwise and problem-specific distance metrics in the linkage tree genetic algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011, pp. 1005–1012. ACM Press, New York (2011)
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: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2009, pp. 851–858. ACM Press, New York (2009)
Rendl, F., Rinaldi, G., Wiegele, A.: Solving Max-Cut to optimality by intersecting semidefinite and polyhedral relaxations. Math. Prog. 121(2), 307 (2010)
Rubinstein, R.Y.: Cross-entropy and rare events for maximal cut and partition problems. ACM Trans. on Modeling and Computer Simulation 12(1), 27–53 (2002)
Thierens, D.: The Linkage Tree Genetic Algorithm. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6238, pp. 264–273. Springer, Heidelberg (2010)
Thierens, D., Bosman, P.A.N.: Optimal mixing evolutionary algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2011, pp. 617–624. ACM Press, New York (2011)
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Bosman, P.A.N., Thierens, D. (2012). On Measures to Build Linkage Trees in LTGA. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32937-1_28
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DOI: https://doi.org/10.1007/978-3-642-32937-1_28
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