A Comparison Between ATNoSFERES And XCSM

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.

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