USING LAYERED GA IN MULTI-AGENT LEARNING

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HABIBI J.A.D.

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Journal Paper

Paper Information

Journal: THE CSI JOURNAL ON COMPUTER SCIENCE AND ENGINEERING Year:2003 | Volume:1 | Issue:2 (b) Page(s): 39-44

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Information Journal Paper

Title

USING LAYERED GA IN MULTI-AGENT LEARNING

Author(s)

HABIBI J.A.D. | Issue Writer Certificate

Keywords

EVOLUTIONARY LEARNINGQ4

MULTI-AGENT SYSTEMSQ4

CONVERGENCE PROBLEMQ4

GENETIC ALGORITHMSQ4

LAYERED GENETIC ALGORITHMQ4

Abstract

In order to successfully apply EVOLUTIONARY LEARNING to the increasing complex Multiagent Systems, we must develop new techniques to improve the performance of the learning process. Evolutionary algorithms, by themselves are very slow; when applied to a complex multiagent system, CONVERGENCE PROBLEM becomes more critical since appropriate coordination strategies between the constituent parts should be achieved and this highly increases the complexity of the problem [1]. Here, we successfully apply a LAYERED GENETIC ALGORITHM to evolve behavioral strategies in a multiagent environment namely The Pursuit problem. Experimental results show that this method outperforms single layer GA and an auction based-traditional algorithm.

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APA: Copy

HABIBI, J.A.D.. (2003). USING LAYERED GA IN MULTI-AGENT LEARNING. THE CSI JOURNAL ON COMPUTER SCIENCE AND ENGINEERING, 1(2 (b)), 39-44. SID. https://sid.ir/paper/70704/en

Vancouver: Copy

HABIBI J.A.D.. USING LAYERED GA IN MULTI-AGENT LEARNING. THE CSI JOURNAL ON COMPUTER SCIENCE AND ENGINEERING[Internet]. 2003;1(2 (b)):39-44. Available from: https://sid.ir/paper/70704/en

IEEE: Copy

J.A.D. HABIBI, “USING LAYERED GA IN MULTI-AGENT LEARNING,” THE CSI JOURNAL ON COMPUTER SCIENCE AND ENGINEERING, vol. 1, no. 2 (b), pp. 39–44, 2003, [Online]. Available: https://sid.ir/paper/70704/en

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