This article describes past and current research efforts in evolutionary robotics that have been carried out at the AnimatLab, Paris. Such approaches entail using an artificial selection process to automatically generate developmental programs for neural networks that control rolling, walking, swimming and flying animats or robots. Basically, they complement the underlying evolutionary process with a developmental procedure – in order hopefully to reduce the size of the genotypic space that is explored – and they occasionally call on an incremental approach, in order to capitalize upon solutions to simpler problems so as to devise solutions to more complex problems. This article successively outlines the historical background of our research, the evolutionary paradigm on which it relies, and the various results obtained so far. It also discusses the potentialities and limitations of the approach and indicates directions for future work.

In this paper, a novel evolutionary algorithm for intrinsic hardware evolution of Field Programmable Gate Array (FPGA) controllers is presented. The main feature of the evolutionary algorithm consists of a mutation operator, in which the mutation rate is defined according to the fitness. Experimental results on a Kephera robot show that the algorithm proposed can successfully navigate the robot to avoid collision in an unknown/changing environment.