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2000
Springer

Controlling Effective Introns for Multi-Agent Learning by Genetic Programming

9 years 5 months ago
Controlling Effective Introns for Multi-Agent Learning by Genetic Programming
This paper presents the emergence of the cooperative behavior for multiple agents by means of Genetic Programming (GP). For the purpose of evolving the effective cooperative behavior, we propose a controlling strategy of introns, which are non-executed code segments dependent upon the situation. The traditional approach to removing introns was able to cope with only a part of syntactically defined introns, which excluded other frequent types of introns. The validness of our approach is discussed with comparative experiments with robot simulation tasks, i.e., a navigation problem and an escape problem.
Hitoshi Iba, Makoto Terao
Added 24 Aug 2010
Updated 24 Aug 2010
Type Conference
Year 2000
Where GECCO
Authors Hitoshi Iba, Makoto Terao
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