Structure Fitness Sharing (SFS) For Evolutionary Design By Genetic Programming
Inhaltsverzeichnis
Reference
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.
DOI
Abstract
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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http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/gecco2002/gecco-2002-14.pdf Gesamt-GP-Workshop siehe S. 780ff
Sonstige Links
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.152.8263
