Rule acquisition with a genetic algorithm

摘要

This paper describes the implementation and the functioning of RAGA (Rule Acquisition with a Genetic Algorithm), a genetic-algorithm-based data mining system suitable for both supervised and certain types of unsupervised knowledge extraction from large and possibly noisy databases. RAGA differs from a standard Genetic Algorithm in seveal crucial respects, including the following: (i) its 'chromosomes' are variable-length symbolic structures, i.e. association rules that may contain n-place predictes (n>=0), (ii) besides typed crossover and mutation operators, it uses macromutations as generalization and specialization operators to efficiently explore the space of rules, and (iii) it evolves a default hierarchy of rules. Several data mining experiments with the system are described.