动态区域性多群体搜索的遗传算法

提出了一种新的动态区域性多群体搜索的遗传算法.该方法的各个遗传群体所占据的 搜索空间由自适应模糊Hamming神经网络的决定,此神经网络通过对遗传个体分类和学习,将 不同的遗传群体分配在搜索空间的不同位置,并可以动态地调整遗传群体的搜索区域或建立新 的遗传群体,从而确保了遗传群体的个体多样性,有效地抑制了可能发生的早熟收敛现象,而且 使得遗传算法具有较强的全局寻优能力和快速局部寻优能力.本文的实验通过对典型的复杂多 模函数的优化计算,也显示了动态区域性多群体搜索的遗传算法的优良性能.

Genetic Algorithm with Dynamic Regional Multi-Species

A novel genetic algorithm with dynamic regional multi species is proposed in this paper. Each of those genetic species occupies a dynamic region that is determined by the weight vector of a fuzzy adaptive Hamming neural network. Through learning and classification of genetic individuals in the evolutionary procedure, the neural network distributes multi species into different regions of the search space. Furthermore, the neural network dynamically expands each search region or establishes new region for good offspring individuals to continuously keep the diversification of genetic population. As a result, the premature problem inherent in genetic algorithms is alleviated and better tradeoff between the ability of exploration and exploitation can be obtained. The experimental results on the optimization computation of typical multi modal functions also have shown good performance of the proposed genetic algorithm.