XCS is a learning classifier system that uses genetic algorithms to evolve a population of classifiers online. When applied to classification problems described by continuous attributes, XCS has demonstrated to be able to evolve classification models-represented as a set of independent interval-based rules-that are, at least, as accurate as those created by some of the most competitive machine learning techniques such as C4.5. Despite these successful results, analyses of how the different genetic operators affect the rule evolution for the interval-based rule representation are lacking. This paper focuses on this issue and conducts a systematic experimental analysis of the effect of the different genetic operators. The observations and conclusions drawn from the analysis are used as a tool for designing new operators that enable the system to extract models that are more accurate than those obtained by the original XCS scheme. More specifically, the system is provided with a new discovery component based on evolution strategies, and a new crossover operator is designed for both the original discovery component and the new one based on evolution strategies. In all these cases, the behavior of the new operators are carefully analyzed and compared with the ones provided by original XCS. The overall analysis enables us to supply important insights into the behavior of different operators and to improve the learning of interval-based rules in real-world domains on average.
In a more comprehensive overview, MoralesOrtigosa et al. REF presented a systematic experimental analysis of the rule discovery component in learning classifier systems.
Analysis and improvement of the genetic discovery component of XCS
Int. J. Hybrid Intell. Syst.
Int. J. Hybrid Intell. Syst.
Computer Science