A Study On Techniques Of Improving Genetic Algorithm And Its Application

Genetic algorithm is an artificial intelligence technology of self-organization and adaptation, which imitates natural organisms' evolutionary process and mechanism to solve problems. Although the algorithm has been widely applied to the computer science, artificial intelligence, information technology and engineering projects, but it still suffers from performance deficiency. So more studies should be carried out to improve its performance because of its large value in theory research and practical application.Presently, the algorithm still suffers from two drawbacks, premature convergence and weak exploitation capabilities. The effective ways to overcome such problems are to maintain the population diversity and enhance exploitation of local search domains.By using the idea mentioned above to solve such problems, three modified schemes of genetic algorithm are proposed in this paper from following aspects:1. A new method of connecting adaptive techniques with genetic algorithm based on the competition between populations is proposed. The method not only can coordinate evolutionary process inside each population by using an adaptive regulation of crossover and mutation operators, but also can advance the formation of-best design by using adaptive adjustment of the competition frequency between populations.2. A new niche hybrid genetic algorithm is proposed, which organically merges the niche technique and simplex method into genetic algorithm. The proposed method not only makes the exploration capabilities of genetic algorithm stronger through niche techniques, but also has more powerful exploitation capabilities by using simplex method.So it effectively alleviates the two major drawbacks of genetic algorithm.3. A new technique for calculating niche radius automatically is proposed, according to the character of the large difference of the niche radii in multi-modal function optimization problems. A set of benchmark functions is used to demonstrate the validity of the method.Finally, the first method mentioned above is applied to the efficiency optimization of a water-supply bumping station, and the simulation reveals its reliability.