Effect of Local Search on Edge Histogram Based Sampling Algorithms for Permutation Problems
Effect of Local Search on Edge Histogram Based Sampling Algorithms for Permutation Problems
复制标题
DOI：
--
复制
发表时间：
2005
期刊：
影响因子：
--
通讯作者：
S. Tsutsui;M. Pelikán;Ashish Ghosh
中科院分区：
文献类型：
--
作者：
S. Tsutsui;M. Pelikán;Ashish Ghosh
专注于消除固定的、与问题无关的遗传算法的缺点的最有前途的研究方向之一是将生成新的候选解视为一个学习问题，并使用选定解的概率模型来生成新的解[5，9，10]。基于对有希望解的概率模型进行学习和采样以生成新的候选解的算法被称为概率建模遗传算法(PMBGA)[9，10]、分布估计算法(EDAS)[7]或迭代密度估计算法(IDEA)[1]。
One of the most promising research directions that focus on eliminating the drawbacks of fixed, problem-independent genetic algorithms, is to look at the generation of new candidate solutions as a learning problem, and use a probabilistic model of selected solutions to generate the new ones [5,9,10]. The algorithms based on learning and sampling a probabilistic model of promising solutions to generate new candidate solutions are called probabilistic model-building genetic algorithms (PMBGAs) [9,10], estimation of distribution algorithms (EDAs) [7], or iterated density estimation algorithms (IDEAs) [1].