The influence of generation alternation model on search performance in deterministic geometric semantic genetic programming

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@InProceedings{Hara:2017:ieeeSMC,

• author = "Akira Hara and Jun-ichi Kushida and Takamichi Yamagata and Tetsuyuki Takahama",
• booktitle = "2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)",
• title = "The influence of generation alternation model on search performance in deterministic geometric semantic genetic programming",
• year = "2017",
• pages = "588--593",
• abstract = "In recent years, semantics-based crossover operators have attracted attention for efficient search in Genetic Programming (GP). Geometric Semantic Genetic Programming (GSGP) is one of the methods, in which a convex combination of two parents is used for creating an offspring. We have previously proposed an improved GSGP, Deterministic GSGP. In Deterministic GSGP, the convex combination is relaxed to an affine combination, and the optimum ratio for the affine combination is determined so that an offspring can always have better fitness than its parents. However, Deterministic GSGP has a problem that search might fall into local optima due to premature convergence. In this paper, we propose a new generation alternation model for maintaining population diversity. In the proposed model, all the individuals have opportunities to generate offspring as parents. We compared our proposed model with the conventional Deterministic GSGP in search performance, and showed its effectiveness.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/SMC.2017.8122670",
• month = oct,
• notes = "Also known as \cite{8122670}",

}

Genetic Programming entries for Akira Hara Jun-ichi Kushida Takamichi Yamagata Tetsuyuki Takahama

Citations