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Evolutionary computing in multi-agent environments: Speciation and symbiogenesis

Bull, Lawrence; Bull, Larry; Fogarty, Terence C.

Authors

Lawrence Bull

Lawrence Bull Larry.Bull@uwe.ac.uk
School Director (Research & Enterprise) and Professor

Terence C. Fogarty



Contributors

Hans-Michael Voigt
Editor

Werner Ebeling
Editor

Ingo Rechenberg
Editor

Hans-Paul Schwefel
Editor

Abstract

© 1996, Springer-Verlag. All rights reserved. In this paper we introduce two macro-level operators to enhance the use of population-based evolutionary computing techniques in multiagent environments: speciation and symbiogenesis. We describe their use in conjunction with the genetic algorithm to evolve Pittsburgh-style classifier systems, where each classifier system represents an agent in a cooperative multi-agent system. The reasons for implementing these kinds of operators are discussed and we then examine their performance in developing a controller for the gait of a wall-climbing quadrupedal robot, where each leg of the quadruped is controlled by a classifier system. We find that the use of such operators can give improved performance over static population/agent configurations.

Citation

Bull, L., Bull, L., & Fogarty, T. C. (1996). Evolutionary computing in multi-agent environments: Speciation and symbiogenesis. Lecture Notes in Artificial Intelligence, 1141, 12-21. https://doi.org/10.1007/3-540-61723-X_965

Journal Article Type Conference Paper
Publication Date Jan 1, 1996
Journal Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Print ISSN 0302-9743
Electronic ISSN 1611-3349
Publisher Springer Verlag
Peer Reviewed Not Peer Reviewed
Volume 1141
Pages 12-21
Series Title Lecture Notes in Computer Science
Series Number 1141
ISBN ;
DOI https://doi.org/10.1007/3-540-61723-X_965
Keywords computation by abstract devices, processor architectures, algorithm analysis and problem complexity, artificial intelligence, computer applications in life sciences
Public URL https://uwe-repository.worktribe.com/output/1105291
Publisher URL http://dx.doi.org/10.1007/3-540-61723-X_965