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Mate Choice in Evolutionary Computation

  • António Leitão
  • Penousal Machado

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.

Keywords

Genetic Programming Sexual Selection Mate Choice Mating Preference Courtship Behaviour 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors acknowledge the financial support from the Future and Emerging Technologies (FET) programme within the Seventh Framework Programme for Research of the European Commission, under the ConCreTe FET-Open project (grant number 611733).

Supplementary material

327421_1_En_7_MOESM1_ESM.xlsx (10 kb)
circlepacking (xlsx 11 kb)
327421_1_En_7_MOESM2_ESM.xlsx (10 kb)
morse (xlsx 10 kb)
327421_1_En_7_MOESM3_ESM.xlsx (9 kb)
regression.xlsx (xlsx 10 kb)

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal

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