遗传算法及其改进设计
摘要………………………………………………………………………………………………………… 2
Abstract……………………………………………………………………………………………………. 2
1、引言…………………………………………………………………………………………………… 3
2、遗传算法简介…………………………………………………………………………………….. 3
2.1 遗传算法的几个基本概念…………………………………………………………. 3
2.2遗传算法的特点………………………………………………………………………….. 4
2.3、遗传算法的发展历程………………………………………………………………… 4
2.3.1 60、70年代的兴起阶段…………………………………………………….. 5
2.3.2 80年代的发展阶段…………………………………………………………. 5
2.3.3 90年代的高潮阶段…………………………………………………………. 6
2.4 遗传算法的应用及前景………………………………………………………………. 6
3、遗传算法的模式定理………………………………………………………………………….. 8
3.1 模式…………………………………………………………………………………………… 8
3.2.1选择算子的作用………………………………………………………………… 9
3.2.2交叉算子的作用………………………………………………………………. 10
3.2.3变异算子的作用………………………………………………………………. 10
4、基本遗传算法存在的问题…………………………………………………………………. 11
4.1基本遗传算法的缺陷…………………………………………………………………. 11
5、各种算子的改进……………………………………………………………………………….. 13
5.1传统的选择操作………………………………………………………………………… 13
5.2交叉操作和变异操作…………………………………………………………………. 14
5.3算例及分析……………………………………………………………………………….. 15
6、总结与展望………………………………………………………………………………………. 17
参考文献……………………………………………………………………………………………….. 19
致谢………………………………………………………………………………………………………. 20
摘要
我们遗传算法及其改进设计,指出遗传算法是一种新兴的搜索寻优技术，遗传算法早在六十年代由J. H. Holland等人提出，并在八十年代得以完善，发展成为标准式的遗传算法，从九十年代中期得到广泛研究与应用。它模拟达尔文的进化论，根据“优胜劣汰”的原则，借助选择、交叉、变异等操作逐步逼近最优解。具有隐并行机制和自适应性，适合于多维，非线性和具有多峰值的问题。遗传算法具有全局优化性和易操作性。最初应用于非数值计算方面，直到进几年才转向全局优化问题，并取得了显著的成果，吸引了越来越多的研究者逐渐成为人工智能领域的一个研究热点。
关键词：遗传算法；改进设计
Abstract
Genetic algorithm is a new optimization technique, which simulates the evolution theory of Darwin, according to the “survival Survival of the fittest “principle, by means of selection, crossover, mutation operation And adaptability, so he is very suitable for multidimensional, nonlinear problems is gradually approaching the optimal solution. With implicit parallel mechanistic multiple peak values. Genetic algorithm first Proposed by J. H. Holland et al in the sixty’s, and perfected in the eighty’s, developed into standard type Genetic algorithms, have been widely researched and used from the mid ninety’s.. Genetic algorithm and global optimization
Easy to operate. Originally applied to non numerical calculation, until in recent years turned to global optimization problems, and take Got notable achievements, has attracted more and more researchers, gradually become a research field of artificial intelligence Hot spot.
Keywords: genetic algorithm; improved design
