Fuzzy genetic algorithms based on level interval algorithm

Many decisions need to be made based on imprecise or incomplete initial information. In such cases, decision makers are generally more interested in sets of the most promising solutions rather than the best single solution. Therefore, in contrast to conventional optimisation approaches that aim to find exact optimal points, we aim to find optimal ranges with variable satisfaction degrees. The paper presents a fuzzy-set-based approach for the representation and optimisation of practical problems with imprecise properties where evolutionary computation is used for obtaining fuzzy solutions through guided searching. The representation of fuzzy sets, its initialisation, crossover, mutation, and validation, the ranking approach for fuzzy objective values, and the propagation method of fuzzy information are discussed. Several examples for illustrating the fuzzy evolutionary optimisation approach are provided