Coevolution of Neural Networks using a Layered Pareto Archive (2006)
The Layered Pareto Coevolution Archive (LAPCA) was recently proposed as an effective Coevolutionary Memory (CM) which, under certain assumptions, approximates monotonic progress in coevolution. In this paper, a technique is developed that interfaces the LAPCA algorithm with NeuroEvolution of Augmenting Topologies (NEAT), a method to evolve neural networks with demonstrated efficiency in game playing domains. In addition, the behavior of LAPCA is analyzed for the first time in a complex game-playing domain: evolving neural network controllers for the game Pong. The technique is shown to keep the total number of evaluations in the order of those required by NEAT, making it applicable to complex domains. Pong players evolved with a LAPCA and with the Hall of Fame (HOF) perform equally well, but the LAPCA is shown to require significantly less space than the HOF. Therefore, combining NEAT and LAPCA is found to be an effective approach to coevolution.
In Proceedings of the Genetic and Evolutionary Computation Conference, 329-336, Seattle, Washington, July 2006. New York, NY: ACM Press.

Risto Miikkulainen Faculty risto [at] cs utexas edu
German Monroy Masters Alumni gmonroy [at] cs utexas edu
Kenneth Stanley Postdoctoral Alumni kstanley [at] cs ucf edu