Mate Choice in Evolutionary Computation

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Antonio Leitao and Penousal Machado",
  title =        "Mate Choice in Evolutionary Computation",
  booktitle =    "Handbook of Genetic Programming Applications",
  publisher =    "Springer",
  year =         "2015",
  editor =       "Amir H. Gandomi and Amir H. Alavi and Conor Ryan",
  chapter =      "7",
  pages =        "155--177",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-20882-4",
  DOI =          "doi:10.1007/978-3-319-20883-1_7",
  abstract =     "Darwin considered two major theories that account for
                 the evolution of species. Natural Selection was
                 described as the result of competition within or
                 between species affecting its individuals relative
                 survival ability, while Sexual Selection was described
                 as the result of competition within species affecting
                 its individuals relative rate of reproduction. This
                 theory emerged from Darwin's necessity to explain
                 complex ornamentation and behaviour that while being
                 costly to maintain, bring no apparent survival
                 advantages to individuals. Mate Choice is one of the
                 processes described by Darwin's theory of Sexual
                 Selection as responsible for the emergence of a wide
                 range of characteristics such as the peacock's tail,
                 bright coloration in different species, certain bird
                 singing or extravagant courtship behaviours. As the
                 theory attracted more and more researchers, the role of
                 Mate Choice has been extensively discussed and backed
                 up by supporting evidence, showing how a force which
                 adapts individuals not to their habitat but to each
                 other can have a strong impact on the evolution of
                 species. While Mate Choice is highly regarded in many
                 research fields, its role in Evolutionary Computation
                 (EC) is still far from being explored and understood.
                 Following Darwin's ideas on Mate Choice, as well as
                 Fisher's contributions regarding the heritability of
                 mating preferences, we propose computational models of
                 Mate Choice, which follow three key rules: individuals
                 choose their mating partners based on their perception
                 mechanisms and mating preferences; mating preferences
                 are heritable the same way as any other trait; Mate
                 Choice introduces its own selection pressure but is
                 subjected to selection pressure itself. The use of
                 self-adaptive methods allows individuals to encode
                 their own mating preferences, use them to evaluate
                 mating candidates and pass preferences on to future
                 generations. Self-adaptive Mate Choice also allows
                 evaluation functions to adapt to the problem at hand as
                 well as to the individuals in the population. In this
                 study we show how Genetic Programming (GP) can be used
                 to represent and evolve mating preferences. In our
                 approach the genotype of each individual is composed of
                 two chromosomes encoding: (1) a candidate solution to
                 the problem at hand (2) a mating partner evaluation
                 function. During the reproduction step of the
                 algorithm, the first parent is chosen based on fitness,
                 as in conventional EC approaches; the mating partner
                 evaluation function encoded on the genotype of this
                 individual is then used to evaluate its potential
                 partners and choose a second parent. Being part of the
                 genotype, the evaluation functions are subjected to
                 evolution and there is an evolutionary pressure to
                 evolve adequate mate evaluation functions. We analyze
                 and discuss the impact of this approach on the
                 evolutionary process, showing how valuable and
                 innovative mate evaluation functions, which would
                 unlikely be designed by humans, arise. We also explain
                 how GP non-terminal and terminal sets can be defined in
                 order to allow the representation of mate selection
                 functions. Finally, we show how self-adaptive Mate
                 Choice can be applied in both academic and real world
                 applications, having achieved encouraging results in
                 both cases. Future venues of research are also proposed
                 such as applications on dynamic environments or
                 multi-objective problems.",

Genetic Programming entries for Antonio Leitao Penousal Machado