The evolution of different neuronal control structures for autonomous agents

Abstract

The use of evolutionary methods to generate controllers for real-world autonomous agents has attracted recent attention. Most of the pertinent research has employed genetic algorithms or variations thereof. Recent research has indicated that the presence of epistasis drastically slows down genetic algorithms. For this reason, this paper uses a different evolutionary method, evolution strategies, for the evolution of various (complex) neuronal control architectures for mobile robots inspired by Braitenberg vehicles. In these experiments, the evolution strategy accelerates the development process by more than an order of magnitude (a few hours compared to more than two days). Furthermore, the evolution strategy yields the same efficacy when applied to receptive-field controllers that require many more parameters than Braitenberg controllers. This dramatic speedup is very important, since the development process is to be done in real robots.