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)
Population sizing for entropy-based model building in discrete estimation of distribution algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
CrossNet: a framework for crossover with network-based chromosomal representations
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Proceedings of the 11th 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
Performance of evolutionary algorithms on random decomposable problems
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Optimal mixing evolutionary algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Pairwise and problem-specific distance metrics in the linkage tree genetic algorithm
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Predetermined versus learned linkage models
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Linkage neighbors, optimal mixing and forced improvements in genetic algorithms
Proceedings of the 14th 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
A review on probabilistic graphical models in evolutionary computation
Journal of Heuristics
On measures to build linkage trees in LTGA
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Evolvability analysis of the linkage tree genetic algorithm
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Variable transformations in estimation of distribution algorithms
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Learning the neighborhood with the linkage tree genetic algorithm
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
On the usefulness of linkage processing for solving MAX-SAT
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Proceedings of the 15th 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 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.