Evolving Order and Chaos: Comparing Particle Swarm Optimization and Genetic Algorithms for Global Coordination of Cellular Automata
This work addresses the problem of optimizing Cellular Automata for computational tasks, but it is incremental as it builds on existing evolutionary algorithms.
The study compared Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) for designing Cellular Automata to solve global coordination tasks, such as density classification and generating chaotic CA, and introduced a new variant called Binary Global-Local PSO (BGL-PSO).
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).