DocumentCode
1569552
Title
A comparison of team evolution operators
Author
Qi, Dehu ; Sun, Ron
Author_Institution
Dept. of Comput. Sci., Lamar Univ., Beaumont, TX, USA
fYear
2004
Firstpage
369
Lastpage
372
Abstract
One of the main research topics in multi-agent systems is learning cooperation among agents. In the MARLBS system, we use genetic algorithms to evolve neural networks, which enhances the cooperation between agents. In this paper, we examine several evolutionary operators to evolve a team, in which team members cooperate with each other to solve problems. The best operators found from experiments efficiently reduce learning time.
Keywords
genetic algorithms; learning (artificial intelligence); multi-agent systems; neural nets; MARLBS system; agents cooperation; evolutionary operators; genetic algorithms; learning cooperation; multi-agent systems; neural networks; team evolution operators; Cognitive science; Computer science; Costs; Evolutionary computation; Genetic algorithms; Learning; Multiagent systems; Neural networks; Neurons; Sun;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Agent Technology, 2004. (IAT 2004). Proceedings. IEEE/WIC/ACM International Conference on
Print_ISBN
0-7695-2101-0
Type
conf
DOI
10.1109/IAT.2004.1342973
Filename
1342973
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