Microgenetic algorithms as generalized hill-climbing operators for GA optimization
Microgenetic algorithms as generalized hill-climbing operators for GA optimization
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DOI：
10.1109/4235.930311
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发表时间：
2001-06-01
影响因子：
14.3
通讯作者：
Petridis, V
中科院分区：
文献类型：
--
作者：
Kazarlis, SA;Papadakis, SE;Petridis, V
在本文中，我们研究了微遗传算法（MGA）[具有小种群和短进化的遗传算法（GA）]作为广义爬山算子的潜力。将标准 GA 与建议的 MGA 算子相结合会产生混合遗传方案 GA-MGA，并具有增强的搜索质量。主 GA 执行全局搜索，而 MGA 探索主 GA 提供的当前解的邻域，寻找更好的解。与尝试沿每个轴独立步骤的传统爬山器相比，MGA 算子执行遗传局部搜索，MGA 的主要优点是它能够识别并遵循任意方向的窄脊，从而实现全局最优。所提出的 GA-MGA 方案针对 13 种不同的方案进行了测试，包括简单的 GA 和具有不同爬山算子的 GA。在包括八个连续变量约束优化问题的测试集上进行了实验，大量的仿真结果证明了所提出的 GA-MGA 方案的效率。对于相同数量的适应度评估，GA-MGA 在解精度、获得解的可行性百分比和鲁棒性方面表现出明显更好的性能。
In this paper, we investigate the potential of a microgenetic algorithm (MGA) [genetic algorithm (GA) with small population and short evolution] as a generalized hill-climbing operator. Combining a standard GA with the suggested MGA operator leads to a hybrid genetic scheme GA-MGA, with enhanced searching qualities. The main GA performs global search while the MGA explores a neighborhood of the current solution provided by the main GA, looking for better solutions. In contrast to conventional hill climbers that attempt independent steps along each axis, the MGA operator performs genetic local search, The major advantage of MGA is its ability to identify and follow narrow ridges of arbitrary direction leading to the global optimum, The proposed GA-MGA scheme is tested against 13 different schemes, including a simple GA and GAs with different hill-climbing operators. Experiments are conducted on a test set including eight constrained optimization problems with continuous variables, Extensive simulation results demonstrate the efficiency of the proposed GA-MGA scheme. For the same number of fitness evaluations, GA-MGA exhibited a significantly better performance in terms of solution accuracy, feasibility percentage of the attained solutions, and robustness.