XCS with stack-based genetic programming

Author

Lanzi, Pier Luca

Author_Institution

Dipt. di Elettronica e Informazione, Politecnico di Milano, Milan, Italy

Volume

2

fYear

2003

fDate

8-12 Dec. 2003

Firstpage

1186

Abstract

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.

Keywords

data structures; genetic algorithms; knowledge based systems; learning (artificial intelligence); learning systems; linear programming; pattern classification; classifier condition representation; learning classifier system; linear programming; mutate classifier; reverse polish notation expression; stack-based genetic programming; virtual stack machine; Artificial intelligence; Computer hacking; Genetic algorithms; Genetic mutations; Genetic programming; Intelligent robots; Laboratories; Learning automata; Proposals; System testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 2003. CEC '03. The 2003 Congress on

Print_ISBN

0-7803-7804-0

Type

conf

DOI

10.1109/CEC.2003.1299803

Filename

1299803