Linkage tree genetic algorithm: first results
Linkage tree genetic algorithm: first results
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DOI：
10.1145/1830761.1830832
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发表时间：
2010-07
期刊：
影响因子：
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通讯作者：
D. Thierens
中科院分区：
文献类型：
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作者：
D. Thierens
我们介绍了链接树遗传算法(LTGA)，这是一种学习问题变量之间联系的称职遗传算法。LTGA使用层次化的聚类算法构建每一代的链接树。为了生成新的子代解决方案，LTGA选择两个父解，并从根开始遍历链接树。在每个分支点，使用由在该特定树节点处合并的簇表示的交叉掩码来重组父对。父代对与子代对竞争，LTGA继续与具有最适合解的对一起遍历链接树。一旦遍历了整个树，当前对的最优解就被复制到下一代。在本文中，我们使用信息度量的归一化变化作为聚类过程的距离度量。对经典的完全欺骗函数的实验结果表明，LTGA只需要非常小的最小种群规模，并且执行的函数评估次数与现有的链接学习遗传算法相似。
We introduce the Linkage Tree Genetic Algorithm (LTGA), a competent genetic algorithm that learns the linkage between the problem variables. The LTGA builds each generation a linkage tree using a hierarchical clustering algorithm. To generate new offspring solutions, the LTGA selects two parent solutions and traverses the linkage tree starting from the root. At each branching point, the parent pair is recombined using a crossover mask represented by the clusters that are merged at that particular tree node. The parent pair competes with the offspring pair, and the LTGA continues traversing the linkage tree with the pair that has the most fit solution. Once the entire tree is traversed, the best solution of the current pair is copied to the next generation. In this paper we use the normalized variation of information metric as distance measure for the clustering process. Experimental results for the classical fully deceptive function show that the LTGA only requires very small, minimal population sizes, and executes a similar number of function evaluations as existing linkage learning genetic algorithms.