一种改进的自适应遗传算法

遗传算法作为一种模仿生物自然进化过程的随机优化算法,对求解一般的全局最优问题具有较好的鲁棒性,而对于解决较复杂的优化问题则存在早熟及稳定性差的缺点。传统的自适应遗传算法虽能有效提高算法的收敛速度,却难以增强算法的鲁棒性。该文提出了一种改进的自适应遗传算法,对交叉率和变异率进行了优化,实现了交叉率和变异率的非线性自适应调整。实验结果表明,相比传统的自适应遗传算法,新算法具有更快的收敛速度和更可靠的稳定性。

An Improved Adaptive Genetic Algorithm

Genetic algorithms(GAs)are stochastic optimization methods using the concept of natural evolution and natural genetics.The standard GA has such disadvantages as premature convergence,low convergence speed and low robustness.Adaptation of parameters and operators is one of the most important and promising areas of research in GA.This paper presents a method for optimal design of an improved adaptive Genetic algorithm.The case study of designing and simulation shows our new method has faster convergence speed and higher robustness.