Host: Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT)
In this paper we show a method for applying a neighboring crossover to QDSEGA for controlling multiple agents. In QDSEGA, a control table for Q-learning is dynamically restructured by a genetic algorithm. In original QDSEGA, the control table is restructured using just a simple crossover. We have already proposed a neighboring crossover to enhance the performance of QDSEGA, though, when we apply our neighboring crossover directly to QDSEGA for controlling multiple agents, it does not give a better performance. In this paper, we combine a simple crossover and a neighboring crossover in order to enhance the performance of QDSEGA. Computer simulation results show that our method reduces the size of the control table by 50 percents compared to QDSEGA with simple crossover.
