Combining robot control strategies using genetic algorithms with memory

Abstract

We use a genetic algorithm augmented with a long term memory to design control strategies for a simulated robot, a mobile vehicle operating in a two-dimensional environment. The simulated robot has five touch sensors, two sound sensors, and two motors that drive locomotive tank tracks. A genetic algorithm trains the robot in several specially-designed simulation environments for evolving basic behaviors such as food approach, obstacle avoidance, and wall following. Control strategies for a more complex environment are then designed by selecting solutions from the stored strategies evolved for basic behaviors, ranking them according to their performance in the new complex environment and introducing them into a genetic algorithm's initial population. This augmented memory-based genetic algorithm quickly combines the basic behaviors and finds control strategies for performing well in the more complex environment.