Bernstein-Bézier Methods for the Computer-Aided Design of Free-Form Curves and Surfaces
Journal of the ACM (JACM)
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Generalized Convergence Models for Tournament- and (mu, lambda)-Selection
Proceedings of the 6th International Conference on Genetic Algorithms
A Mathematical Analysis of Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
On the complexity of hierarchical problem solving
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Difficulty of linkage learning in estimation of distribution algorithms
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Dependency structure matrix, genetic algorithms, and effective recombination
Evolutionary Computation
Building-block identification by simultaneity matrix
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
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 14th annual conference on Genetic and evolutionary computation
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Estimation of distribution algorithms (EDAs) identify linkages among genes and build models which decompose a given problem. EDAs have been successfully applied to many real-world problems; however, whether their models indicate the optimal way to decompose the given problem is rarely studied. This paper proposes using the number of function evaluations (N"f"e) as the performance measure of EDA models. As a result, the optimal model can be defined as the one that consumes the fewest N"f"e on average for EDAs to solve a specific problem. Based on this concept, correct building blocks (BBs) can be defined as groups of genes that construct the optimal model. Similarly, linkages within a BB are defined as the correct linkages of which the specific problem consists. The capabilities of four commonly used linkage-learning metrics, nonlinearity, entropy, simultaneity and differential mutual complement, are investigated based on the above definitions. For certain partially separable problems, none of the above metrics yields difference that is statistically significant between linear and nonlinear gene pairs. Although an optimal threshold still exists to separate linear and nonlinear gene pairs, most existing EDA designs today have not yet characterize such threshold. Based on the idea of N"f"e estimation, this paper also proposes a metric enhancer, named eNFE, to enhance existing linkage-learning techniques. Empirical results show that eNFE improves BB identification by eliminating spurious linkages which occur often in most existing EDAs.