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Abstract: This paper is about the evolutionary design of multi-agent systems. An important part of recent research in this domain has been focusing on collaborative coevolutionary methods. We expose the possible drawbacks of these methods, and show that for a non-trivial problem called the "Blind mice" problem, a classical GA approach in which whole populations are evaluated, selected and crossed together (with a few tweaks) finds an elegant and non-intuitive solution more efficiently than cooperative... (Update)
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BibTeX entry: (Update)
@inproceedings{ miconi03when,
author = "Thomas Miconi",
title = "When Evolving Populations is Better than Coevolving Individuals: The Blind Mice Problem" ,
booktitle="Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI '03)",
editor="Georg Gottlob and Toby Walsh",
publisher="Morgan Kaufmann",
year="2003",
url = "citeseer.ist.psu.edu/miconi03when.html" }
Citations (may not include all citations):1051 Optimization and Machine Learning (context) - Goldberg, in - 1989
471 Genetic Programming - Koza - 1992
153 Robocup: The robot world cup initiative - Kitano, Asada et al. - 1995
75 Cognitive systems based on adaptive algorithms (context) - Holland, Reitman - 1978
58 A cooperative coevolutionary approach to function optimizati.. - Potter, DeJong - 1994
44 Incremental evolution of complex general behavior - Gomez, Mikkulainen - 1997
17 Genetic programming produced competitive soccer softbot team.. - Luke - 1998
6 An empirical analysis of collaboration methods in cooperativ.. - Wiegand, Liles et al. - 2001
6 Cooperative coevolution of multi-agent systems - Yong, Mikkulainen - 2001
1 A collective genetic algorithm (context) - Miconi - 2001
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