A function optimization method based on improved genetic algorithm and its performance analysis

It usually comes to the function optimization problem during the process of application of engineering. When many problems are mathematic modeled, they can be abstracted as a function optimization. It's approved that Genetic Algorithm is an effective computing way to function optimization. An improved Genetic Algorithm was proposed in tills paper. The algorithm adopts multi-individual-crossover way, takes the threshold value in the process of crossover and mutation, thus expand the search space of solution and keep the variety of the population in the course of optimizing, and increase the probability of obtaining optimum solution Through testing a series of typical functions, the results validate the effectiveness of the proposed algorithm.