This paper presents a novel genetic algorithm for globally solving un-constraint optimization problem. In this algorithm, a new real coded crossover operator is proposed firstly. Furthermore, for improving the convergence speed and the searching ability of our algorithm, the good point set theory rather than random selection is used to generate the initial population, and the chaotic search operator is adopted in the best solution of the current iteration. The experimental results tested on numerical benchmark functions show that this algorithm has excellent solution quality and convergence characteristics, and performs better than some algorithms.

中文翻译：

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一种新的全局优化遗传算法

本文提出了一种用于全局求解无约束优化问题的新型遗传算法。该算法首先提出了一种新的实编码交叉算子。此外，为了提高算法的收敛速度和搜索能力，采用优良点集理论而不是随机选择来生成初始种群，并在本次迭代的最佳解中采用混沌搜索算子。在数值基准函数上测试的实验结果表明，该算法具有优良的求解质量和收敛特性，性能优于某些算法。