Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Probabilistic Incremental Program Evolution: Stochastic Search Through Program Space
ECML '97 Proceedings of the 9th European Conference on Machine Learning
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Characterizing complex product architectures: Regular Paper
Systems Engineering
Building-block Identification by Simultaneity Matrix
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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 matrix approach for finding extrema: problems with modularity, hierarchy, and overlap
A matrix approach for finding extrema: problems with modularity, hierarchy, and overlap
Linkage identification by non-monotonicity detection for overlapping functions
Evolutionary Computation
Sporadic model building for efficiency enhancement of the hierarchical BOA
Genetic Programming and Evolvable Machines
Using previous models to bias structural learning in the hierarchical BOA
Proceedings of the 10th annual conference on Genetic and evolutionary computation
From mating pool distributions to model overfitting
Proceedings of the 10th annual conference on Genetic and evolutionary computation
iBOA: the incremental bayesian optimization algorithm
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Enhancing the Efficiency of the ECGA
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Combinatorial effects of local structures and scoring metrics in bayesian optimization algorithm
Proceedings of the first ACM/SIGEVO Summit 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
Dependency structure matrix, genetic algorithms, and effective recombination
Evolutionary Computation
Effective linkage learning using low-order statistics and clustering
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
A new DSM clustering algorithm for linkage groups identification
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Learning factorizations in estimation of distribution algorithms using affinity propagation
Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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Detecting multivariate interactions between the variables of a problem is a challenge in traditional genetic algorithms (GAs). This issue has been addressed in the literature as the linkage learning problem. It is widely acknowledged that the success of GA in solving any problem depends on the proper detection of multivariate interactions in the problem. Different approaches have thus been proposed to detect and represent such interactions. Estimation of distribution algorithms (EDAs) are amongst these approaches that have been successfully applied to a wide range of hard optimization problems. They build a model of the problem to detect multivariate interactions, but the model building process is often computationally intensive. In this paper, we propose a new clustering algorithm that turns pair-wise interactions in a dependency structure matrix (DSM) into an interaction model efficiently. The model building process is carried out before the evolutionary algorithm to save computational burden. The accurate interaction model obtained in this way is then used to perform an effective recombination of building blocks (BBs) in the GA. We applied the proposed approach to solve exemplar hard optimization problems with different types of linkages to show the effectiveness and efficiency of the proposed approach. Theoretical analysis and experiments showed that the building of an accurate model requires O(nlog (n)) number of fitness evaluations. The comparison of the proposed approach with some existing algorithms revealed that the efficiency of the model building process is enhanced significantly.