Offspring generation method using delaunay triangulation for real-coded genetic algorithms

Authors:
Hisashi Shimosaka;Tomoyuki Hiroyasu;Mitsunori Miki
Affiliations:
Graduate School of Engineering, Doshisha University, Kyoto, Japan;Department of Engineering, Doshisha University;Department of Engineering, Doshisha University
Venue:
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Year:
2006

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Abstract

To design crossover operators with high search ability in real-coded Genetic Algorithms, it will be efficient to utilize both information regarding the parent distribution and the landscape of the objective function. Here, we propose a new offspring generation method using Delaunay triangulation. The proposed method can concentrate offspring in regions with a satisfactory evaluation value, inheriting the parent distribution. Through numerical examples, the proposed method was shown to be capable of deriving the optimum with a smaller population size and lower number of evaluations than Simplex Crossover, which uses only information of the parent distribution.