Incollection,
Coevolutionary Fitness Switching: Learning Complex Collective Behaviors Using Genetic Programming
Advances in Genetic Programming 3, chapter 18, MIT Press, Cambridge, MA, USA, (June 1999)
Abstract
Genetic programming provides a useful paradigm for
developing multiagent systems in the domains where
human programming alone is not sufficient to take into
account all the details of possible situations.
However, existing GP methods attempt to evolve
collective behavior immediately from primitive actions.
More realistic tasks require several emergent behaviors
and a proper coordination of these is essential for
success. We have recently proposed a framework, called
fitness switching, to facilitate learning to coordinate
composite emergent behaviors using genetic programming.
Coevolutionary fitness switching described in this
chapter extends our previous work by introducing the
concept of coevolution for more effective
implementation of fitness switching. Performance of the
presented method is evaluated on the table transport
problem and a simple version of simulated robot soccer
problem. Simulation results show that coevolutionary
fitness switching provides an effective mechanism for
learning complex collective behaviors which may not be
evolved by simple genetic programming.
