XCS with stack-based genetic programming
Pier Luca Lanzi · 2004
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.