Strongly Typed Genetic Programming in Evolving Cooperation Strategies
Thomas D. Haynes, Roger L. Wainwright, Sandip Sen, Dale A. Schoenefeld · 1995
A key concern in genetic programming (GP) is the size of the state--space which must be searched for large and complex problem domains. One method to reduce the state--space size is by using Strongly Typed Genetic Programming (STGP). We applied both GP and STGP to construct cooperation strategies to be used by multiple predator agents to pursue and capture a prey agent on a grid--world. This domain has been extensively studied in Distributed Artificial Intelligence (DAI) as an easy--to--describe but difficult--to--solve cooperation problem. The evolved programs from our systems are competitive with manually derived greedy algorithms. In particular the STGP paradigm evolved strategies in which the predators were able to achieve their goal without explicitly sensing the location of other predators or communicating with other predators. This is an improvement over previous research in this area. The results of our experiments indicate that STGP is able to evolve programs that perform sign...