关键词: 遗传算法, 数值优化问题, 混合杂交算子, 适应度函数

Abstract: This paper proposes a novel genetic algorithm for numerical optimization problems with continuous variables, in which a hybrid crossover operator is designed to improve the fitness of individuals by means of combining traditional crossover operators with a new optimization technique, as well as a modified fitness function. Moreover, it is demonstrated that the new algorithm is globally convergent. The numerical results show the proposed algorithm more efficiently enhance GA than do the methods in the literatures on these test functions.

Key words: Genetic algorithms, Numerical optimization problems, Hybrid crossover operator, Fitness functions