Evolving Order and Chaos: Comparing Particle Swarm Optimization and Genetic Algorithms for Global Coordination of Cellular Automata

TL;DR

This paper compares Particle Swarm Optimization and Genetic Algorithms in designing Cellular Automata for complex tasks, introducing a new PSO variant and analyzing their efficiency in evolving coordinated CA behaviors.

Contribution

It introduces a new Binary Global-Local PSO variant and compares its effectiveness with GAs in evolving cellular automata for global coordination tasks.

Findings

PSO and GAs differ in search efficiency for CA design

BGL-PSO outperforms traditional PSO variants in experiments

Both algorithms successfully evolve CA for density classification and chaos generation

Abstract

We apply two evolutionary search algorithms: Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) to the design of Cellular Automata (CA) that can perform computational tasks requiring global coordination. In particular, we compare search efficiency for PSO and GAs applied to both the density classification problem and to the novel generation of 'chaotic' CA. Our work furthermore introduces a new variant of PSO, the Binary Global-Local PSO (BGL-PSO).