Revisiting the personal satellite assistant
Boye Annfelt Høverstad · 2007
This paper revisits the evolution of a neural controller for asimulated Personal Satellite Assistant (PSA) using the En-forced Sub-Populations (ESP) neuroevolutionary algorithm, as described by Sit et al. in 2005 [8].ESP has previously been shown to be a very efficient algo-rithm for neuroevolution. As opposed to the original paper,we are not primarily concerned with the solutions discov-ered by the system, but rather with how ESP performs itsevolutionary search; using the unstable PSA control task asa vehicle for fitness evaluation. We propose several changes to the original ESP algorithm. Our experiments suggest that these improve both the inter-nal consistency, and the success rate of the algorithm.We further analyze the ability of ESP to go beyond classicweight evolution. We compare our evolutionary results with those of a simple hill-climb algorithm, and propose that im-proved heuristics for the modifications of network topologyin ESP may be necessary to evolve increasingly complex androbust controllers.