J4 ›› 2008, Vol. 30 ›› Issue (9): 53-57.
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向佐勇[1,2] 刘正才[2]
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摘要:
标准遗传算法的求泛能力优于它的求精能力,在求解GA-困难问题时求解精度难以控制,本文由此提出了一种改进的ε-混合遗传算法。本算法在每代找出最优个体之后,以该最优个体为初始出发点在一个固定半径的区域内进行局部搜索,以搜索结果代替最差个体或其它个体,然后再进入下一代操作。算法大大提高了求解精度,同时也提高了稳定定性。
关键词: 标准遗传算法 局部搜索 &epsilon, -混合遗传算法 局部搜索半径
Abstract:
The extensive ability of SGA surpasses its refinement ability. It is difficult to control the precision when we solve the GA-hard problems. This paper proposes a new hybrid genetic algorithm,ε-HGAA. After finding the most optimal individual in each generation, it takes this optimal individual as th initial point to carry out local search in a fixed-radius region. The search results replace the worst individual or other individuals; and the algorithm enters the next generation operation again. This algorithm not only increases the solution precision, but also enhances the stability.
Key words: SGA, local seareh, ε-HGAA, radius of local search
向佐勇[1,2] 刘正才[2]. 一种改进的混合遗传算法研究[J]. J4, 2008, 30(9): 53-57.
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http://joces.nudt.edu.cn/CN/Y2008/V30/I9/53