Linkage Discovery through Data Mining [Research Frontier
- 19 January 2010
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Computational Intelligence Magazine
- Vol. 5 (1), 10-13
- https://doi.org/10.1109/MCI.2009.935310
Abstract
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.Keywords
This publication has 9 references indexed in Scilit:
- Linkage Analysis in Genetic AlgorithmsStudies in Computational Intelligence, 2008
- A crossover for complex building blocks overlappingPublished by Association for Computing Machinery (ACM) ,2006
- Modeling Dependencies of Loci with String Classification According to Fitness DifferencesLecture Notes in Computer Science, 2004
- Linkage identification based on epistasis measures to realize efficient genetic algorithmsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Data mining in soft computing framework: a surveyIEEE Transactions on Neural Networks, 2002
- Data Mining and Knowledge Discovery with Evolutionary AlgorithmsPublished by Springer Science and Business Media LLC ,2002
- Linkage Identification by Non-monotonicity Detection for Overlapping FunctionsEvolutionary Computation, 1999
- FDA -A Scalable Evolutionary Algorithm for the Optimization of Additively Decomposed FunctionsEvolutionary Computation, 1999
- Mining association rules between sets of items in large databasesPublished by Association for Computing Machinery (ACM) ,1993