摘要
针对遗传算法的遗传效率问题,引入不动点理论的"剖分-标号-剖分"思想,通过寻找全标单纯形来对最优解进行定位,对全标单纯形再次剖分,寻找其内部的全标单纯形,使最优解得范围进一步缩小。遗传算法按相对适应度大小随机选取全标单纯形内的点作为初始化群体,极大地提高了遗传算法的效率。将遗传变异区间化,锁定在全标单纯形内或附近单纯形,使得最优解的精确度也得到极大地提高。
The fixed point theory of the ＂split - label - split＂ idea is introduced into the genetic al- gorithms to solve genetic efficiency, locking the optimal solution looking for completely labeled sim- plexes, finding their internal completely labeled simplexes in the resubdivision of simplexes in the previous step make the optimal solution regions further reduced. Genetic Algorithms randomly select points in the completely labeled simplexes as the initial group in the relative size of the fitness, which greatly improved the efficiency of genetic algorithm. Genetic variation occurred in completely labeled simplexes or near them, which makes the precision of the optimal solution greatly improved.
出处
《河北工程大学学报（自然科学版）》
CAS
2013年第3期67-70,共4页
Journal of Hebei University of Engineering:Natural Science Edition
基金
国家自然科学基金(11272112)
河北省自然科学基金(E2012402061)
关键词
遗传算法
不动点
剖分
全标单纯形
整数标号
genetic algorithm
fixed point
Split
completely labeled simplexes
integer label
