Genetic algorithms with multi-parent recombination
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Multiparent recombination in evolutionary computing
Advances in evolutionary computing
Using Uncorrelated Discriminant Analysis for Tissue Classification with Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Classification consistency analysis for bootstrapping gene selection
Neural Computing and Applications
Gene selection using genetic algorithm and support vectors machines
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on neural networks for pattern recognition and data mining
Multi-parent extension of partially mapped crossover for combinatorial optimization problems
Expert Systems with Applications: An International Journal
An effective gene selection method based on relevance analysis and discernibility matrix
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Evaluating a local genetic algorithm as context-independent local search operator for metaheuristics
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Fuzzy Set Theory and Applications; Guest Editors: Ferdinand Chovanec, Olga Nánásiová, Alexander Šostak
Informative gene selection and tumor classification by null space LDA for microarray data
ESCAPE'07 Proceedings of the First international conference on Combinatorics, Algorithms, Probabilistic and Experimental Methodologies
Hybridising harmony search with a Markov blanket for gene selection problems
Information Sciences: an International Journal
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The microarray data classification problem is a recent complex pattern recognition problem. The most important goal in supervised classification of microarray data, is to select a small number of relevant genes from the initial data in order to obtain high predictive classification accuracy. With the framework of a hybrid filter-wrapper, we study in this paper the role of the multi-parent recombination operator. For this purpose, we introduce a Random Multi Parent crossover (RMPX) and we analyze their effects in a genetic algorithm (GA) which is combined with Fisher's Linear Discriminant Analysis (LDA). This hybrid algorithm has the major characteristic that the GA uses not only a LDA classifier in its fitness function, but also LDA's discriminant coefficients to integrate a multi-parent specialized crossover and mutation operation to improve the performance of gene selection. In the experimental results it is observed that RPMX operator work very well by achieving lower classification error rates.