  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).
