An Analysis of Geometric Semantic Crossover: A Computational Geometry Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.4031

  author =       "Mauro Castelli and Luca Manzoni and Ivo Goncalves and 
                 Leonardo Vanneschi and Leonardo Trujillo and 
                 Sara Silva",
  title =        "An Analysis of Geometric Semantic Crossover: A
                 Computational Geometry Approach",
  booktitle =    "Proceedings of the 8th International Joint Conference
                 on Computational Intelligence, IJCCI (ECTA) 2016",
  year =         "2016",
  pages =        "201--208",
  publisher =    "Scitepress",
  keywords =     "genetic algorithms, genetic programming, Semantics,
                 Convex Hull",
  isbn13 =       "978-989-758-201-1",
  DOI =          "doi:10.5220/0006056402010208",
  abstract =     "Geometric semantic operators have recently shown their
                 ability to outperform standard genetic operators on
                 different complex real world problems. Nonetheless,
                 they are affected by drawbacks. In this paper, we focus
                 on one of these drawbacks, i.e. the fact that geometric
                 semantic crossover has often a poor impact on the
                 evolution. Geometric semantic crossover creates an
                 offspring whose semantics stands in the segment joining
                 the parents (in the semantic space). So, it is
                 intuitive that it is not able to find, nor reasonably
                 approximate, a globally optimal solution, unless the
                 semantics of the individuals in the population contains
                 the target. In this paper, we introduce the concept of
                 convex hull of a genetic programming population and we
                 present a method to calculate the distance from the
                 target point to the convex hull. Then, we give
                 experimental evidence of the fact that, in four
                 different real-life test cases, the target is always
                 outside the convex hull. As a consequence, we show that
                 geometric semantic crossover is not helpful in those
                 cases, and it is not even able to approximate the
                 population to the target. Finally, in the last part of
                 the paper, we propose ideas for future work on how to
                 improve geometric semantic crossover.",

Genetic Programming entries for Mauro Castelli Luca Manzoni Ivo Goncalves Leonardo Vanneschi Leonardo Trujillo Sara Silva