Convergence Time for the Linkage Learning Genetic Algorithm
Evolutionary Computation
Building-block Identification by Simultaneity Matrix
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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
Linkage identification by non-monotonicity detection for overlapping functions
Evolutionary Computation
Dependency structure matrix, genetic algorithms, and effective recombination
Evolutionary Computation
A new DSM clustering algorithm for linkage groups identification
Proceedings of the 12th annual conference on Genetic and evolutionary computation
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In this paper, we present a perturbation-based linkage identification algorithm that employs a novel metric to detect linkages. The proposed metric is a combination of linearity and multiplicative relationship. The proposed method is called Interaction Detection for Hybrid Decomposable Problems (IDHDP) algorithm. Our algorithm can be applied to the additive and multiplicative decomposable problems and problems that have both kind of decomposability, i.e. hybrid decomposability. By using IDHDP, an interaction matrix is computed that represents the degree of interaction between pairs of loci. To extract linkage groups from the interaction matrix, a local threshold is calculated for each variable by the two-means algorithm. We apply IDHDP to problems with different types of decomposability. A comparison with some existing algorithms shows the efficiency and effectiveness of IDHDP.