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应用遗传算法原理确定函数的最优解
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2009-08-14
李勇
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本文主要介绍了一种新型的、随机性的全局优化方法即遗传算法。一般应用于在一个
问题的解集中查找最优解情况,如是一个问题有多个答案,但是想查找一个最优答案的话,那么使用遗传算法可以达到更快更好的效果。即在浮点编码遗传算法中加入一个函数,构成适于不可微函数全局优化的遗传算法。该算法改善了遗传算法的局部搜索能力,显著提高了遗传算法求得全局解的概率。
关键词: 遗传算法; 函数极值; 全局最优
Abstract: A hybrid computational intellective algorithm for locating the global optima of in
differentiable nonlinear function was put forward by setting the Powell algorithm in real-code
genetic algorithm. The hybrid approach improved the local searching ability of the genetic
algorithm and promoted the probability for the global optima greatly. Because only the objective values are used, the hybrid approach is a generalized genetic algorithm for global optima of differentiable and indifferentiable nonlinear functions.
Keywords: genetic algorithms; the function pole value; global optima
问题的解集中查找最优解情况,如是一个问题有多个答案,但是想查找一个最优答案的话,那么使用遗传算法可以达到更快更好的效果。即在浮点编码遗传算法中加入一个函数,构成适于不可微函数全局优化的遗传算法。该算法改善了遗传算法的局部搜索能力,显著提高了遗传算法求得全局解的概率。
关键词: 遗传算法; 函数极值; 全局最优
Abstract: A hybrid computational intellective algorithm for locating the global optima of in
differentiable nonlinear function was put forward by setting the Powell algorithm in real-code
genetic algorithm. The hybrid approach improved the local searching ability of the genetic
algorithm and promoted the probability for the global optima greatly. Because only the objective values are used, the hybrid approach is a generalized genetic algorithm for global optima of differentiable and indifferentiable nonlinear functions.
Keywords: genetic algorithms; the function pole value; global optima
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