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Effects of Module Encapsulation in Repetitively Modular Genotypes on the Search Space
Ivan I. Garibay1,2, Ozlem O. Garibay1,2, and Annie S. Wu1
1University of Central Florida, School of Computer Science, P.O. Box 162362, Orlando, FL 32816-2362, USA
igaribay@cs.ucf.edu
ozlem@cs.ucf.edu
aswu@cs.ucf.edu
http://ivan.research.ucf.edu
2University of Central Florida, Office of Research, Orlando Tech Center/ Research Park, 12443 Research Parkway Orlando, FL 32826, USA.
Abstract. We introduce the concept of modularity-preserving representations. If a representation is modularity-preserving, the existence of modularity in the problem space is translated into a corresponding modularity in the search space. This kind of representation allows us to analyze the impact of modularity at the genomic level. We investigate the question of what constitutes a module at the genomic level of evolutionary search and provide a static analysis of how to identify good and bad modules based on their ability to reduce the search space, thus, biasing the search space towards a solution. We also prove, under a set of assumptions, that the systematic encapsulation of lower order modules into higher order modules does not change the size or bias of a search space and that this process produces a hierarchy of equivalent search spaces.
LNCS 3102, p. 1125 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2004