Approaching evolutionary robotics through population-based incremental learning

Abstract

Population-based incremental learning (PBIL) is a recently developed evolutionary computing technique based on concepts found in genetic algorithms and competitive learning-based artificial neural networks. PBIL and a traditional genetic algorithm are compared on the task of evolving a neural network-based controller for a simulated robotic agent. In particular, this paper examines the performance of FP-PBIL, a variant of PBIL developed for this task that works with floating-point representations rather than bit-strings. Results are presented showing the superior performance of FP-PBIL. This advantage, combined with lower memory and processing requirements indicate that the technique is well-suited to developing online, evolutionary controllers for autonomous robotic agents.