The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Conquering hierarchical difficulty by explicit chunking: substructural chromosome compression
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications (Studies in Computational Intelligence)
Comparing clusterings---an information based distance
Journal of Multivariate Analysis
Population sizing for entropy-based model building in discrete estimation of distribution algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A new method for linkage learning in the ECGA
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Linkage tree genetic algorithms: variants and analysis
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Hierarchical problem solving with the linkage tree genetic algorithm
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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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.