Structure Fitness Sharing (SFS) For Evolutionary Design By Genetic Programming

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Jianjun Hu and Kisung Seo and Shaobo Li and Zhun Fan and Ronald C. Rosenberg and Erik D. Goodman: Structure Fitness Sharing (SFS) For Evolutionary Design By Genetic Programming. GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 780-787, Morgan Kaufmann Publishers, 9-13 July 2002.



Balanced structure and parameter search is critical to evolutionary design with genetic programming (GP). Structure Fitness Sharing (SFS), based on a structure labeling technique, is proposed to maintain the structural diversity of a population and combat premature convergence of structures. SFS achieves balanced structure and parameter search by applying fitness sharing to each unique structure in a population, to prevent takeover by the best structure and thereby maintain the diversity of both structures and parameters simultaneously. SFS does not require definition of a distance metric among structures, and is thus more universal and efficient than other fitness sharing methods for GP. The effectiveness of SFS is demonstrated on a real-world bond-graph-based analog circuit synthesis problem.

Extended Abstract


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