Adaptive Evolutionary Genetic Algorithms on a Class of Combinatorial Optimization Problems

Sheng Zhong, Baihai Zhang, Qiao Li, Jun Yu Li, Zhiwei Lin
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Abstract

This paper investigates an adaptive evolutionary genetic algorithm on combinatorial optimization problem, where the solution space can be organized in form of a subset tree. A kind of genetic gene uniform encode scheme and adaptive evolution idea are used before proceeding crossover operation, and crossover is achieved between the current and previous generations individual. The orthogonal table approach is utilized to produce initial population, which can satisfy the multiplicity of the initial population. Two examples are provided to illustrate the effectiveness of the proposed methods.
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一类组合优化问题的自适应进化遗传算法
研究了组合优化问题的一种自适应进化遗传算法,该算法的解空间可以用子集树的形式组织。在进行交叉操作之前,采用了一种遗传基因统一编码方案和自适应进化思想,实现了当前和前代个体之间的交叉。利用正交表法生成初始种群,满足初始种群的多重性。给出了两个实例来说明所提方法的有效性。
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