Evolving Complex Group Behaviors Using Genetic Programming with Fitness Switching

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{Zhang:2000:ALR,

• author = "Byoung-Tak Zhang and Dong-Yeon Cho",
• title = "Evolving Complex Group Behaviors Using Genetic Programming with Fitness Switching",
• journal = "Artificial Life and Robotics",
• year = "2000",
• volume = "4",
• number = "2",
• pages = "103--108",
• keywords = "genetic algorithms, genetic programming",
• URL = "http://bi.snu.ac.kr/Publications/Journals/International/AROB4-2.ps",
• URL = "http://citeseer.ist.psu.edu/454877.html",
• abstract = "Genetic programming provides a useful tool for emergent computation and artificial life. However, conventional genetic programming is not efficient enough to solve realistic multiagent tasks consisting of several emergent behaviours that need to be coordinated in proper sequence. In this paper, we describe a novel method, called fitness switching, for evolving composite cooperative behaviours of multiple robotic agents using genetic programming. The method maintains a pool of basis fitness functions which are switched from simpler ones to more complex ones. The performance is demonstrated and compared in the context of a table transport problem. Experimental results show that the fitness switching method is an effective mechanism for evolving collective behaviours which may not be solved by simple genetic programming.",

}

Genetic Programming entries for Byoung-Tak Zhang Dong-Yeon Cho

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