Geometric Semantic Crossover with an Angle-aware Mating Scheme in Genetic Programming for Symbolic Regression

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@InProceedings{Chen:2017:EuroGP,
  author =       "Qi Chen and Bing Xue and Yi Mei and Mengjie Zhang",
  title =        "Geometric Semantic Crossover with an Angle-aware
                 Mating Scheme in Genetic Programming for Symbolic
                 Regression",
  booktitle =    "EuroGP 2017: Proceedings of the 20th European
                 Conference on Genetic Programming",
  year =         "2017",
  month =        "19-21 " # apr,
  editor =       "Mauro Castelli and James McDermott and 
                 Lukas Sekanina",
  series =       "LNCS",
  volume =       "10196",
  publisher =    "Springer Verlag",
  address =      "Amsterdam",
  pages =        "229--245",
  organisation = "species",
  keywords =     "genetic algorithms, genetic programming: Poster",
  DOI =          "doi:10.1007/978-3-319-55696-3_15",
  abstract =     "Recent research shows that incorporating semantic
                 knowledge into the genetic programming (GP)
                 evolutionary process can improve its performance. This
                 work proposes an angle-aware mating scheme for
                 geometric semantic crossover in GP for symbolic
                 regression. The angle-awareness guides the crossover
                 operating on parents which have a large angle between
                 their relative semantics to the target semantics. The
                 proposed idea of angle-awareness has been incorporated
                 into one state-of-the-art geometric crossover, the
                 locally geometric semantic crossover. The experimental
                 results show that, compared with locally geometric
                 semantic crossover and the regular GP crossover, the
                 locally geometric crossover with angle-awareness not
                 only has a significantly better learning performance
                 but also has a notable generalisation gain on unseen
                 test data. Further analysis has been conducted to see
                 the difference between the angle distribution of
                 crossovers with and without angle-awareness, which
                 confirms that the angle-awareness changes the original
                 distribution of angles by decreasing the number of
                 parents with zero degree while increasing their
                 counterparts with large angles, leading to better
                 performance.",
  notes =        "Part of \cite{Castelli:2017:GP} EuroGP'2017 held
                 inconjunction with EvoCOP2017, EvoMusArt2017 and
                 EvoApplications2017",
}

Genetic Programming entries for Qi Chen Bing Xue Yi Mei Mengjie Zhang

Citations