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Direct Evolution of Hierarchical Solutions with Self-Emergent Substructures

By Xin Li, Chi Zhou, Weimin Xiao and Peter C. Nelson


Linear genotype representation and modularity have continuously received extensive attention from the Genetic Programming (GP) community. The advantages of a linear genotype include a convenient and efficient implementation scheme. However, most existing techniques using a linear genotype follow the imperative programming language paradigm and a direct hierarchical composition for the functionality of the solution is underachieved. Our work is based on Prefix Gene Expression Programming (P-GEP), a new GP method featured by a prefix notation based linear genotype representation. Since P-GEP uses a functional language paradigm, its framework results in natural selfemergence of substructures as functional components during the evolution. We propose to preserve and utilize potentially useful emergent substructures via a dynamic substructure library, empowering the algorithm to focus the search on a higher level of the solution structure. Preliminary experiments on the benchmark regression problems have shown the effectiveness of this approach. 1

Year: 2008
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