Deterministic Crossover Based on Target Semantics in Geometric Semantic Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Hara:2016:IIAI-AAI,
  author =       "A. Hara and J. I. Kushida and R. Tanemura and 
                 T. Takahama",
  booktitle =    "2016 5th IIAI International Congress on Advanced
                 Applied Informatics (IIAI-AAI)",
  title =        "Deterministic Crossover Based on Target Semantics in
                 Geometric Semantic Genetic Programming",
  year =         "2016",
  pages =        "197--202",
  abstract =     "In this paper, we focus on solving symbolic regression
                 problems, in which we find functions approximating the
                 relationships between given input and output data.
                 Genetic Programming (GP) is often used for evolving
                 tree structural numerical expressions. Recently, new
                 crossover operators based on semantics of tree
                 structures have attracted many attentions for efficient
                 search. In the semantics-based crossover, offspring is
                 created from its parental individuals so that the
                 offspring can be similar to the parents not
                 structurally but semantically. Geometric Semantic
                 Genetic Programming (GSGP) is a method in which
                 offspring is produced by a convex combination of two
                 parental individuals. In order to improve the search
                 performance of GSGP, we propose an improved Geometric
                 Semantic Crossover using the information of the target
                 semantics. In conventional GSGP, ratios of convex
                 combinations are determined at random. On the other
                 hand, our proposed method can use optimal ratios for
                 affine combinations of parental individuals. We
                 confirmed that our method showed better performance
                 than conventional GSGP in several symbolic regression
                 problems.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/IIAI-AAI.2016.220",
  month =        jul,
  notes =        "Also known as \cite{7557602}",
}

Genetic Programming entries for Akira Hara Jun-ichi Kushida R Tanemura Tetsuyuki Takahama

Citations