Fuzzy connectives based crossover operators to model genetic algorithms population diversity

Genetic algorithms are adaptive methods which may be used to solve search and optimization problems. Genetic algorithms process a population of search space solutions with three operations: selection, crossover and mutation. An important problem in the use of genetic algorithms is the premature convergence in a local optimum. Their main causes are the lack of diversity in the population and the disproportionate relationship between exploitation and exploration. The crossover operator is considered one of the most determinant elements for solving this problem. In this paper, we present new crossover operators based on fuzzy connectives for real-coded genetic algorithms. These operators are designed to avoid the premature convergence problem. To do so, they should keep the right exploitation/exploration balance to suitably model the diversity of the population. (C) 1997 Elsevier Science B.V.