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

Authors

Anthony D. Rhodes, Portland State UniversityFollow

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

Locate the Document

https://doi.org/10.1109/CEC48606.2020.9185828

DOI

10.1109/CEC48606.2020.9185828

Persistent Identifier

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

Publisher

IEEE

Citation Details

Rhodes, A. D. (2020). Evolving Order and Chaos: Comparing Particle Swarm Optimization and Genetic Algorithms for Global Coordination of Cellular Automata. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/cec48606.2020.9185828