Forming Neural Networks Through Efficient and Adaptive Coevolution

Author:

Moriarty David E.1,Miikkulainen Risto2

Affiliation:

1. Information Sciences Institute University of Southern California 4676 Admiralty Way Marina del Rey, CA 90292

2. Department of Computer Sciences The University of Texas at Austin Austin, TX 78712

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.

Publisher

MIT Press - Journals

Subject

Computational Mathematics

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