Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A crossover for complex building blocks overlapping
Proceedings of the 8th annual conference on Genetic and 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
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
Research frontier: memetic computation-past, present & future
IEEE Computational Intelligence Magazine
Estimating meme fitness in adaptive memetic algorithms for combinatorial problems
Evolutionary Computation
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
Multi-objective optimization with estimation of distribution algorithm in a noisy environment
Evolutionary Computation
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Genetic algorithms (GAs) are extensively adopted in various aspects of data mining, e.g., association rules, clustering, and classification. Instead of applying GAs for data mining, this study addresses linkage discovery, an essential topic in GAs, by using data mining methods. Inspired by natural evolution, GAs utilize selection, crossover, and mutation operations to evolve candidate solutions into global optima. This evolutionary scheme can effectively resolve many search and optimization problems. As the most salient feature of GAs, crossover enables the recombination of good parts of two selected chromosomes, yet, in doing so, may disrupt the collected promising segments.