Extracted global structure makes local building block processing effective in XCS
Martin V. Butz, Martin Pelikán, Xavier Llorà, David E. Goldberg · 2005
Michigan-style learning classifier systems (LCSs), such as the accuracy-based XCS system, evolve distributed problem solutions represented by a population of rules. Recently, it was shown that decomposable problems may require effective processing of subsets of problem attributes, which cannot be generally assured with standard crossover operators. A number of competent crossover operators capable of effective identification and processing of arbitrary subsets of variables or string positions were proposed for genetic and evolutionary algorithms. This paper effectively introduces two competent crossover operators to XCS by incorporating techniques from competent genetic algorithms (GAs): the extended compact GA (ECGA) and the Bayesian optimization algorithm (BOA). Instead of applying standard crossover operators, here a probabilistic model of the global population is built and sampled to generate offspring classifiers locally. Various offspring generation methods are introduced and evaluated. Results indicate that the performance of the proposed learning classifier systems XCS/ECGA and XCS/BOA is similar to that of XCS with informed crossover operators that is given all information about problem structure on input and exploits this knowledge using problem-specific crossover operators.