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Forming Neural Networks Through Efficient And Adaptive Coevolution (1997)
David E. Moriarty
and
Risto Miikkulainen
This article demonstrates the advantages of a cooperative, coevolutionary search in difficult control problems. The 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, more adaptive, and 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.
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Citation:
Evolutionary Computation
, Vol. 5 (1997), pp. 373--399.
Bibtex:
@article{moriarty:ec97, title={Forming Neural Networks Through Efficient And Adaptive Coevolution}, author={David E. Moriarty and Risto Miikkulainen}, volume={5}, journal={Evolutionary Computation}, pages={373--399}, url="http://www.cs.utexas.edu/users/ai-lab?moriarty:ec97", year={1997} }
People
Risto Miikkulainen
Faculty
risto [at] cs utexas edu
David E. Moriarty
Ph.D. Alumni
moriarty [at] alumni utexas net
Areas of Interest
Evolutionary Computation
Neuroevolution
Reinforcement Learning
Robotics
Labs
Neural Networks