Evolving Order and Chaos: Comparing Particle Swarm Optimization and Genetic Algorithms for Global Coordination of Cellular Automata
Artificial Intelligence
2019-09-10 v1 Neural and Evolutionary Computing
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).
Keywords
Cite
@article{arxiv.1909.03560,
title = {Evolving Order and Chaos: Comparing Particle Swarm Optimization and Genetic Algorithms for Global Coordination of Cellular Automata},
author = {Anthony D. Rhodes},
journal= {arXiv preprint arXiv:1909.03560},
year = {2019}
}