hLCGA:混合竞争协同进化遗传算法

2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06) Pub Date : 2006-12-13 DOI:10.1109/HIS.2006.32

Grégoire Danoy, P. Bouvry, T. Martins

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引用次数: 11

摘要

本文介绍了一种新的混合协同进化算法，称为hLCGA (hybrid松耦合遗传算法)，它将竞争协同进化遗传算法与局部搜索算法相结合。我们将其应用于Rosenbrock函数优化问题，并将五种混合变量的结果与原始LCGA进行了比较。我们展示了混合协同进化算法与局部搜索算法在解质量和收敛速度方面的优势。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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hLCGA: A Hybrid Competitive Coevolutionary Genetic Algorithm

We introduce in this article a new hybrid coevolutionary algorithm called hLCGA (hybrid Loosely Coupled Genetic Algorithm) that consists in combining a competitive coevolutionary genetic algorithm and a local search algorithm. We apply it to the Rosenbrock function optimization problem and compare the results of five hybrid variants to the original LCGA. We show the advantages of hybridizing a coevolutionary algorithm with local search algorithms in terms of solution quality and convergence speed.

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来源期刊

2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06)

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