A Comparison between ATNoSFERES and XCSM
Samuel Landau, Sébastien Picault, Olivier Sigaud, Pierre Gérard · 2002
In this paper we present ATNoSFERES, a new framework based on an indirect encoding Genetic Algorithm which builds finite-state automata controllers able to deal with perceptual aliasing. We compare it with XCSM, a memory-based extension of the most studied Learning Classifier System, XCS, through a benchmark experiment. We then discuss the assets and drawbacks of ATNoSFERES in the context of that comparison.