Dynamic Fuzzy Logic Control of Genetic Algorithm Probabilities

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Genetic algorithms are commonly used to solve combinatorial optimization problems. The implementation evolves using genetic operators (crossover, mutation, selection, etc.). Anyway, genetic algorithms like some other methods have parameters (population size, probabilities of crossover and mutation) which need to be tune or chosen. In this paper, our project is based on an existing hybrid genetic algorithm working on the multiprocessor scheduling problem. We propose a hybrid Fuzzy- Genetic Algorithm (FLGA) approach to solve the multiprocessor scheduling problem. The algorithm consists in adding a fuzzy logic controller to control and tune dynamically different parameters (probabilities of crossover and mutation), in an attempt to improve the algorithm performance. For this purpose, we will design a fuzzy logic controller based on fuzzy rules to control the probabilities of crossover and mutation. Compared with the Standard Gene...