Dynamic and heuristic fuzzy connectives-based crossover operators for controlling the diversity and convergence of real-coded genetic algorithms

Genetic algorithms are adaptive methods which may be used as approximation heuristic for search and optimization problems. Genetic algorithms process a population of search space solutions with three operations: selection, crossover and mutation. A great problem in the use of genetic algorithms is the premature convergence, a premature stagnation of the search caused by the lack of diversity in the population and a 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 two types of crossover operators based on fuzzy connectives for real-coded genetic algorithms. The rst type is designed to keep a suitable sequence between the exploration and the exploitation along the GA's run, the dynamic fuzzy connectives-based crossover operators, the second, for generating oospring near to the best parents in order to ooer diversity or convergence in a prootable way, the heuristic fuzzy connectives-based crossover operators. We combine both crossover operators for designing dynamic heuristic fuzzy connectives-based crossover operators that show a robust behaviour.