一维全局最优问题的改进遗传算法

遗传算法是一种借鉴自然界生物自然选择和自然遗传机制的高度并行、随机及自适应的搜索算法，该算法对一般的全局最优有良好的鲁棒性。但是，对非线性较强的函数，简单的遗传算法的收敛速度较慢，稳定性差。本文提出了一种新操作：一点交换和两点交换相结合、普通变异和大变异相结合的操作。理论证明和数值计算结果表明，该算法是有效的。

An Improved Genetic Algorithm for One dimensional Global Optimization problem

Genetic Algorithms(GAs) are the highly parallel,random and self adaptive search procedures based on the mechanics of natural selection and natural genetics of organism in natural world,and has good robustness.With the increasingly wide applications of GAs,their instinct drawback and the extraordinarily slow convergence become the biggest obstacle of their further acception in many application fields.The paper proposes a new operation method which combines one point crossover with two point crossover and ordinary mutation with Big Mutation Operation(BMO).The theory proof and numerical computing results show that the method is valid.