XCS with Stack-Based Genetic Programming
نویسنده
چکیده
We present an extension of the learning classifier system XCS in which classifier conditions are represented by RPN expressions and stack-based Genetic Programming is used to recombine and mutate classifiers. In contrast with other extensions of XCS involving tree-based Genetic Programming, the representation we apply here produces conditions that are linear programs, interpreted by a virtual stack machine (similar to a pushdown automaton), and recombined through standard genetic operators. We test the version of XCS extended with stack-based conditions on a set of problems of different complexity.
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تاریخ انتشار 2006