In this paper we study the impact of GAs’ components such as encoding, different crossover, mutation and replacement strategies on the number of extracted association rules and their quality. Moreover,
we propose a strategy to manage the population. The later is organized in classes where each one encloses same size rules. Each class can be seen as a population on which a GA is applied. All tests are conducted on two types of benchmarks : synthetic and real ones of different sizes.
