An Improved Crossover Operator of Genetic Algorithm

Crossover operation is the main means of Genetic Algorithms, for the lack of crossover operation, from three aspects of crossover operation, systemically proposed one kind of improved Crossover operation of Genetic Algorithms, namely used a kind of new consistent Crossover Operator and determined which two individuals to be paired for crossover based on relevance index, which can enhance the algorithm´s global searching ability; Based on the concentrating degree of fitness, a kind of adaptive crossover probability can guarantee the population will not fall into a local optimal result. Simulation results show that: Compared with the traditional cross-adaptive genetic Algorithms and other adaptive genetic algorithm, the new algorithm´s convergence velocity and global searching ability are improved greatly, the average optimal results and the rate of converging to the optimal results are better.