The Linkage Tree Genetic Algorithm

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 defined by the clustering 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 fully deceptive functions and nearest neighbor NK-landscape problems with tunable overlap show that the LTGA can solve these hard functions efficiently without knowing the actual position of the linked variables on the problem representation.

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