an algorithm for distributed on-line, on-board evolutionary robotics
This paper presents part of an endeavour towards robots and robot collectives that can adapt their controllers autonomously and self-sufficiently and so independently learn to cope with situations unforeseen by their designers. We introduce the Embodied Distributed Evolutionary Algorithm (DEA) for on-line, on-board adaptation of robot controllers. We experimentally evaluate DEA using a number of well-known tasks in the evolutionary robotics field to determine whether it is a viable implementation of on-line, on-board evolution. We compare it to the encapsulated mu + 1 ON- LINE algorithm in terms of (the stability of) task performance and the sensitivity to parameter settings. Experiments show that DEA provides an effective method for on-line, on-board adaptation of robot controllers. Compared to mu + 1 ON- LINE, in terms of performance there is no clear winner, but in terms of sensitivity to parameter settings and stability of performance DEA is significantly better than mu + 1 ON- LINE.
evolution robotics controllers on-line evolution adaptive behaviour