A self-adaptive Mate Choice Model for Symbolic Regression



Sexual Selection through Mate Choice has for the past few decades attracted the attention of any researchers from different fields. Numerous contributions and supporting evidence for the role and impact of Sexual Selection through Mate Choice in Evolution have emerged since then. Just like Evolutionary Theory has had to adapt its models to account for Sexual Selection through Mate Choice and its effects, it is relevant to study and analyse the impact that Mate Choice may have on Evolutionary Algorithms.
In this study we describe a nature inspired self-adaptive Mate Choice approach designed to tackle Symbolic Regression problems. Results on a set of test functions are presented and compared to a standard approach, showing that Mate Choice is able to contribute to enhanced results on complex instances of Symbolic Regression. Also, the resulting behaviours are contrasted and discussed, suggesting that Mate Choice is able to evolve Mating evaluation functions that are able to select partners in meaningful and valuable ways.


Evolutionary Computation, Genetic Programming, Sexual Selection, Mate Choice, Self-adaption, Symbolic Regression


Self-adaptive Mate Choice


IEEE Congress on Evolutionary Computation (CEC), June 2013

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