Forming neural networks through efficient and adaptive coevolution

Authors:
David E. Moriarty;Risto Miikkulainen
Affiliations:
Information Sciences Institute University of Southern California 4676 Admiralty Way Marina del Rey, CA 90292 moriarty@isi.edu;Department of Computer Sciences The University of Texas at Austin Austin, TX 78712 risto@cs.utexas.edu
Venue:
Evolutionary Computation
Year:
1997

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Abstract

This article demonstrates the advantages of a cooperative, coevolutionary search in difficult control problems. The symbiotic adaptive neuroevolution (SANE) system coevolves a population of neurons that cooperate to form a functioning neural network. In this process, neurons assume different but overlapping roles, resulting in a robust encoding of control behavior. SANE is shown to be more efficient and more adaptive and to maintain higher levels of diversity than the more common network-based population approaches. Further empirical studies illustrate the emergent neuron specializations and the different roles the neurons assume in the population.