Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Machine Learning
On the complexity of hierarchical problem solving
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
CrossNet: a framework for crossover with network-based chromosomal representations
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Optinformatics for schema analysis of binary genetic algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A surrogate-assisted linkage inference approach in genetic algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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Genetic algorithms and the descendant methods have been deemed robust and practical. To enhance the capabilities of genetic algorithms, tremendous effort has been invested in the field of evolutionary computation. One of the major trends to enhance genetic algorithms is to extract and exploit the relationship among variables, such as estimation of distribution algorithms and perturbation-based methods. In this study, we make an attempt to enable inductive linkage identification (ILI) to detect general problem structures, in which one variable may link to an arbitrary number of other variables. Our results indicate that the proposed technique can successfully detect the given problem structure.