Title

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

Published In

2020 IEEE Congress on Evolutionary Computation (CEC)

Document Type

Citation

Publication Date

9-3-2020

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).

Rights

©2020 IEEE

DOI

10.1109/CEC48606.2020.9185828

Persistent Identifier

https://archives.pdx.edu/ds/psu/34435

Publisher

IEEE

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