In this paper we study the influence of the exploration/exploitation balance on the performances of a real binary/like genetic algorithm in automatically generating fuzzy knowledge bases from a set of numerical data. The influence is explored through different scheduling of crossover strategies throughout the evolution process. The aim of this paper is to prove the influence of a good balance between exploration and exploitation levels on the performances of the optimization algorithm used, along with the influence of a good definition of the early stages versus the late stages of the evolution.
|Title of host publication||Annual Meeting of the North American Fuzzy Information Processing Society (IEEE)|
|Publication status||Published - 2004|
|Event||2004 Annual Meeting of the North American Fuzzy Information Processing Society - Banff, Canada|
Duration: 27 Jun 2004 → 30 Jun 2004
|Conference||2004 Annual Meeting of the North American Fuzzy Information Processing Society|
|Period||27/06/2004 → 30/06/2004|