A UNIVERSAL ECLECTIC GENETIC ALGORITHM FOR CONSTRAINED OPTIMIZATION

A UNIVERSAL ECLECTIC GENETIC ALGORITHM FOR CONSTRAINED OPTIMIZATION
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一种通用的约束优化折衷遗传算法

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通讯作者：
Carlos Quezada
Carlos Quezada
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作者：
Ángel Fernando Kuri Morales;Carlos Quezada

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过去已经介绍了几种求解非线性约束优化问题的非传统方法：进化规划、遗传算法和模拟退火法等。然而，这些方法都存在一些局限性。在本文中，我们描述了一种脱离正则遗传算法(CGA)的遗传算法，它实现了文献中所称的理想化遗传算法(IGA)的大部分特征。这种遗传算法是为接近免疫遗传算法而设计的，我们称之为折衷遗传算法(EGA)。我们描述了一种特殊遗传算法在最近文献中讨论的一组六个约束非线性问题的求解中的应用。并与CGA和精英遗传算法(TGA)的性能进行了比较。
Several non-traditional methods of solving nonlinear constrained optimization problems have been introduced in the past: evolutionary programming, genetic algorithms and simulated annealing, among others. However these methods present some limitations. In this paper we describe a GA which departs from canonical GAs (CGA) and achieves most of the characteristics of what, in the literature, has been called an Idealized GA (IGA). This GA is designed to approach the IGA and we have it called an Eclectic Genetic Algorithm (EGA). We describe the application of an EGA to the solution of a set of six constrained nonlinear problems which have been recently discussed in the literature. Also the performance of an EGA is compared those of the CGA and eliTist GA (TGA).