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Building self-adaptive and self-organizing (SASO) systems is a challengingproblem, in part because SASO principles are not yet well understood and fewplatforms exist for exploring them. Cellular automata (CA) are a well-studiedapproach to exploring the principles underlying self-organization. A CAcomprises a lattice of cells whose states change over time based on a discreteupdate function. One challenge to developing CA is that the relationship of anupdate function, which describes the local behavior of each cell, to the globalbehavior of the entire CA is often unclear. As a result, many researchers haveused stochastic search techniques, such as evolutionary algorithms, toautomatically discover update functions that produce a desired global behavior.However, these update functions are typically defined in a way that does notprovide for self-adaptation. Here we describe an approach to discovering CAupdate functions that are both self-adaptive and self-organizing. Specifically,we use a novel evolutionary algorithm-based approach to discover finite statemachines (FSMs) that implement update functions for CA. We show how thisapproach is able to evolve FSM-based update functions that perform well on thedensity classification task for 1-, 2-, and 3-dimensional CA. Moreover, we showthat these FSMs are self-adaptive, self-organizing, and highly scalable, oftenperforming well on CA that are orders of magnitude larger than those used toevaluate performance during the evolutionary search. These results demonstratethat CA are a viable platform for studying the integration of self-adaptationand self-organization, and strengthen the case for using evolutionaryalgorithms as a component of SASO systems.

Leveraging Evolutionary Search to Discover Self-Adaptive and Self-Organizing Cellular Automata