An Introduction to Geometric Semantic Genetic Programming

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  author =       "Leonardo Vanneschi",
  title =        "An Introduction to Geometric Semantic Genetic
  booktitle =    "NEO 2015: Results of the Numerical and Evolutionary
                 Optimization Workshop NEO 2015 held at September 23-25
                 2015 in Tijuana, Mexico",
  year =         "2015",
  editor =       "Oliver Schuetze and Leonardo Trujillo and 
                 Pierrick Legrand and Yazmin Maldonado",
  volume =       "663",
  series =       "Studies in Computational Intelligence",
  pages =        "3--42",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, semantic
                 genetic programming",
  isbn13 =       "978-3-319-44003-3",
  DOI =          "doi:10.1007/978-3-319-44003-3_1",
  abstract =     "For all supervised learning problems, where the
                 quality of solutions is measured by a distance between
                 target and output values (error), geometric semantic
                 operators of genetic programming induce an error
                 surface characterized by the absence of locally
                 suboptimal solutions (unimodal error surface). So,
                 genetic programming that uses geometric semantic
                 operators, called geometric semantic genetic
                 programming, has a potential advantage in terms of
                 evolvability compared to many existing computational
                 methods. This fosters geometric semantic genetic
                 programming as a possible new state-of-the-art machine
                 learning methodology. Nevertheless, research in
                 geometric semantic genetic programming is still much in
                 demand. This chapter is oriented to researchers and
                 students that are not familiar with geometric semantic
                 genetic programming, and are willing to contribute to
                 this exciting and promising field. The main objective
                 of this chapter is explaining why the error surface
                 induced by geometric semantic operators is unimodal,
                 and why this fact is important. Furthermore, the
                 chapter stimulates the reader by showing some promising
                 applicative results that have been obtained so far. The
                 reader will also discover that some properties of
                 geometric semantic operators may help limiting
                 overfitting, bestowing on genetic programming a very
                 interesting generalization ability. Finally, the
                 chapter suggests further reading and discusses open
                 issues of geometric semantic genetic programming.",
  notes =        "Published 2017",

Genetic Programming entries for Leonardo Vanneschi