Symbiotic Combination as an Alternative to Sexual Recombination in Genetic Algorithms
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
On the complexity of hierarchical problem solving
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Population sizing for entropy-based model building in discrete estimation of distribution algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Optimal implementations of UPGMA and other common clustering algorithms
Information Processing Letters
A new method for linkage learning in the ECGA
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Linkage tree genetic algorithm: first results
Proceedings of the 12th annual conference companion 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
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
More concise and robust linkage learning by filtering and combining linkage hierarchies
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Hi-index | 0.00 |
Hierarchical problems represent an important class of nearly decomposable problems. The concept of near decomposability is central to the study of complex systems. When little a priori information is available, a black box problem solver is needed to optimize these hierarchical problems. The solver should be able to learn linkage information, and to preserve and test partial solutions at different levels in the hierarchy. Two well known benchmark functions - shuffled Hierarchical If-And-Only-If (HIFF) and shuffled Hierarchical Trap (HTRAP) functions - exemplify the challenges posed by hierarchical problems. Standard genetic algorithms are unable to solve these problems, and specific methods, like SEAM and hBOA, have been designed to address them. In this paper, we investigate how the recently developed Linkage Tree Genetic Algorithm (LTGA) performs on these hierarchical problems. We compare LTGA with SEAM and hBOA on HIFF and HTRAP functions. Results show that, although LTGA is a simple algorithm compared to SEAM and hBOA, it nevertheless is a very efficient, reliable, and scalable algorithm for solving the randomly shuffled versions of HIFF and HTRAP, two hard, hierarchical problems.