Abstract
Correlations between alleles after selection are an important source of information. Such correlations should be exploited for further search and thereby constitute the building blocks of evolutionary exploration. With this background we analyze the structure of the offspring probability distribution, or exploration distribution, for a simple GA with mutation only and a crossover GA and compare them to Estimation-Of- Distribution Algorithms (EDAs). This will allow a precise characterization of the structure of exploration w.r.t. correlations in the search distribution for these algorithms. We find that crossover transforms, depending on the crossover mask, mutual information between loci into entropy. In total, it can only decrease such mutual information. In contrast, the objective of EDAs is to estimate the correlations between loci and exploit this information during exploration. This may lead to an effective increase of mutual information in the exploration distribution, what we define correlated exploration.
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References
S. Baluja. Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning. Technical Report CMU-CS-94-163, Comp. Sci. Dep., Carnegie Mellon U., 1994.
S. Baluja and S. Davies. Using optimal dependency-trees for combinatorial optimization: Learning the structure of the search space. In Proceedings of the Fourteenth Int. Conf. on Machine Learning (ICML 1997), pages 30–38, 1997.
J.S. de Bonet, C.L. Isbell, Jr., and P. Viola. MIMIC: Finding optima by estimating probability densities. In M. C. Mozer, M. I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems, volume 9, page 424. The MIT Press, 1997.
G. Halder, P. Callaerts, and W. Gehring. Induction of ectopic eyes by targeted expression of the eyeless gene in Drosophila. Science, 267:1788–1792, 1995.
J. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, USA, 1975.
J.H. Holland. Building blocks, cohort genetic algorithms, and hyperplane-defined functions. Evolutionary Computation, 8:373–391, 2000.
M. Pelikan, D.E. Goldberg, and E. Cantú-Paz. Linkage problem, distribution estimation, and Bayesian networks. Evolutionary Computation, 9:311–340, 2000.
M. Pelikan, D.E. Goldberg, and F. Lobo. A survey of optimization by building and using probabilistic models. Technical Report IlliGAL-99018, Illinois Genetic Algorithms Laboratory, 1999.
M. Toussaint. Demonstrating the evolution of complex genetic representations: An evolution of artificial plants. In 2003 Genetic and Evolutionary Computation Conference (GECCO 2003), 2003. In this volume.
M. Toussaint. On the evolution of phenotypic exploration distributions. In C. Cotta, K. De Jong, R. Poli, and J. Rowe, editors, Foundations of Genetic Algorithms 7 (FOGA VII). Morgan Kaufmann, 2003. In press.
M.D. Vose. The Simple Genetic Algorithm. MIT Press, Cambridge, 1999.
G.P. Wagner and L. Altenberg. Complex adaptations and the evolution of evolvability. Evolution, 50:967–976, 1996.
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Toussaint, M. (2003). The Structure of Evolutionary Exploration: On Crossover, Buildings Blocks, and Estimation-Of-Distribution Algorithms. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_17
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DOI: https://doi.org/10.1007/3-540-45110-2_17
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