Abstract
Genetic programming provides a useful tool for emergent computation and artificial life research. However, conventional genetic programming is not efficient enough to solve realistic multiagent tasks consisting of several emergent behaviors that need to be coordinated in the proper sequence. In this paper, we describe a novel method, called fitness switching, for evolving composite cooperative behaviors in 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 evaluated in the context of a table transport problem. Experimental results show that the fitness switching method is an effective mechanism for evolving collective behaviors which can not be solved by simple genetic programming.
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Zhang, BT., Cho, DY. Evolving complex group behaviors using genetic programming with fitness switching. Artif Life Robotics 4, 103–108 (2000). https://doi.org/10.1007/BF02480864
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DOI: https://doi.org/10.1007/BF02480864