Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Probabilistic Incremental Program Evolution: Stochastic Search Through Program Space
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Proceedings of the 5th International Conference on Genetic Algorithms
On the complexity of hierarchical problem solving
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Linkage learning, overlapping building blocks, and systematic strategy for scalable recombination
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Gene Expression and Fast Construction of Distributed Evolutionary Representation
Evolutionary Computation
Linkage identification based on epistasis measures to realize efficient genetic algorithms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
Linkage identification by non-monotonicity detection for overlapping functions
Evolutionary Computation
Efficient linkage discovery by limited probing
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Population sizing of dependency detection by fitness difference classification
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
Empirical investigations on parallel competent genetic algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Improving the design of sequences for DNA computing: A multiobjective evolutionary approach
Applied Soft Computing
| Hi-index | 0.01 |
Genetic Algorithms perform crossovers effectively when linkage sets -- sets of variables tightly linked to form building blocks -- are identified. Several methods have been proposed to detect the linkage sets. Perturbation methods (PMs) investigate fitness differences by perturbations of gene values and Estimation of distribution algorithms (EDAs) estimate the distribution of promising strings. In this paper, we propose a novel approach combining both of them, which detects dependencies of variables by estimating the distribution of strings clustered according to fitness differences. The proposed algorithm, called the Dependency Detection for Distribution Derived from fitness Differences (D5), can detect dependencies of a class of functions that are difficult for EDAs, and requires less computational cost than PMs.