Evolution of Cooperating ANNs Through Functional Phenotypic Affinity
This work deals with the problem of automatically obtaining ANNs that cooperate in modelling of complex functions. We propose an algorithm where the combination of networks takes place at the phenotypic operational level. Thus, we evolve a population of networks that are automatically classified into different species depending on the performance of their phenotype, and individuals of each species cooperate forming a group to obtain a complex output. The components that make up the groups are basic ANNs (primitives) and could be reused in other search processes as seeds or could be combined to generate new solutions. The magnitude that reflects the difference between ANNs is their affinity vector, which must be automatically created and modified. The main objective of this approach is to model complex functions such as environment models in robotics or multidimensional signals.
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