Proceedings of the 5th International Conference on Genetic Algorithms
Optimal implementations of UPGMA and other common clustering algorithms
Information Processing Letters
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
The linkage tree genetic algorithm
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Genetic and Evolutionary Computation Conference
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
On the usefulness of linkage processing for solving MAX-SAT
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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The linkage tree genetic algorithm (LTGA) learns, each generation, a linkage model by building a hierarchical cluster tree. The LTGA is an instance of the more general gene-pool optimal mixing evolutionary algorithm (GOMEA) that uses a family of subsets (FOS) linkage model. We compare the performance of the linkage model learning LTGA with several predetermined FOS linkage models applied by GOMEA. The predetermined models are matched to the underlying problem structure of four benchmark functions: onemax, deceptive trap functions, maximal overlapping nearest-neighbor NK-landscapes, and weighted MAXCUT problems. Although the a priori fixed models are specifically designed to capture the interactions between the problem variables, experimental results show that - for problems with intricate interaction structure - they are actually less efficient than LTGA that dynamically learns a hierarchical tree model. Some of these observations were unexpected and raise the question of what exactly is the optimal linkage structure for a given problem as used by GOMEA. In the case of the NK-problem a linkage model that is an accurate description of the underlying additively decomposable fitness structure is clearly not an optimal linkage model. Being able to rebuild the linkage model each generation has clear benefits above using fixed, predetermined linkage models.