Evolution of communication with a spatialized genetic algorithm

Abstract

We extend previous work by modeling evolution of communication using a spatialized genetic algorithm which recombines strategies purely locally. Here cellular automata are used as a spatialized environment in which individuals gain points by feeding from drifting food sources and are 'harmed' if they fail to hide from migrating predators. Our individuals are capable of making one of two arbitrary sounds, heard only locally by their immediate neighbors. They can respond to sounds from their neighbors by opening their mouths or by hiding. By opening their mouths in the presence of food they maximize gains; by hiding when a predator is present they minimize losses. We consider the result a 'natural' template for benefits from communication; unlike a range of other studies, it is here only the recipient of communicated information that immediately benefits. A community of'perfect communicators' could be expected to make a particular sound when successfully feeding, responding to that same sound from their neighbors by opening their mouths. They could be expected to make a different sound when 'hurt' and respond to that second sound from their neighbors by hiding. Suppose one starts from a small set of 'Adam and Eve' strategies randomized across a cellular automata array, and uses a genetic algorithm which operates purely locally by cross-breeding strategies with their most successful neighbors. Can one, in such an environment, expect evolution of local communities of 'perfect communicators'? With some important qualifications, the answer is 'yes'.

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