Controlling Effective Introns for Multi-Agent Learning by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Iba:2000:GECCO,

• author = "Hitoshi Iba and Makoto Terao",
• title = "Controlling Effective Introns for Multi-Agent Learning by Genetic Programming",
• pages = "419--426",
• year = "2000",
• publisher = "Morgan Kaufmann",
• booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000)",
• editor = "Darrell Whitley and David Goldberg and Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer",
• address = "Las Vegas, Nevada, USA",
• publisher_address = "San Francisco, CA 94104, USA",
• month = "10-12 " # jul,
• keywords = "genetic algorithms, genetic programming",
• ISBN = "1-55860-708-0",
• URL = "http://gpbib.cs.ucl.ac.uk/gecco2000/GP191.pdf",
• URL = "http://citeseer.ist.psu.edu/478951.html",
• abstract = "This paper presents the emergence of the cooperative behavior for multiple agents by means of Genetic Programming (GP). For the purpose of evolving the e#ective 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.",
• notes = "A joint meeting of the ninth International Conference on Genetic Algorithms (ICGA-2000) and the fifth Annual Genetic Programming Conference (GP-2000) Part of \cite{whitley:2000:GECCO}",

}

Genetic Programming entries for Hitoshi Iba Makoto Terao

Citations