A Coevolutionary Approach to Learning
Sequential Decision Rules
Mitchell A. Potter
Kenneth A. De Jong
John J. Grefenstette
We present a coevolutionary approach to learning sequential decision rules which appears to have a number of advantages over non-coevolutionary approaches. The coevolutionary approach encourages the formation of stable niches representing simpler subbehaviors. The evolutionary direction of each subbehavior can be controlled independently, providing an alternative to evolving complex behavior using intermediate training steps. Results are presented showing a significant learning rate speedup over a noncoevolutionary approach in a simulated robot domain. In addition, the results suggest the coevolutionary approach may lead to emergent problem decompositions.