Offspring Generation Method Using Delaunay Triangulation for Real-Coded Genetic Algorithms

To design crossover operators with high search ability in realcoded 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.