On Evolving Social Systems: Communication, Speciation and Symbiogenesis

Larry Bull ()
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Larry Bull: University of the West of England

Computational and Mathematical Organization Theory, 1999, vol. 5, issue 3, No 5, 302 pages

Abstract: Abstract In this paper we introduce three enhancements for evolutionary computing techniques in social environments. We describe the use of the genetic algorithm to evolve communicating rule-based systems, where each rule-based system represents an agent in a social/multi-agent environment. It is shown that the evolution of multiple cooperating agents can give improved performance over the evolution of an equivalent single agent, i.e. non-social, system. We examine the performance of two social system configurations as approaches to the control of gait in a wall climbing quadrupedal robot, where each leg of the quadruped is controlled by a communicating agent. We then introduce two social-level operators&2014;speciation and symbiogenesis&2014;which aim to reduce the amount of knowledge required a priori by automatically manipulating the system&2018;s social structure and describe their use in conjunction with the communicating rule-based systems. The reasons for implementing these kinds of operators are discussed and we then examine their performance in developing the controller of the wall-climbing quadruped. We find that the use of such operators can give improved performance over static population/agent configurations.

Keywords: classifier system; coevolution; genetic algorithm; multi-agent system; quadrupedal robot gait (search for similar items in EconPapers)
Date: 1999
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DOI: 10.1023/A:1009642524130

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