a hybrid gene selection algorithm for microarray cancer classification using genetic algorithm and learning automata

Cancer classification is an important problem in cancer diagnosis and treatment. One of the most effective methods in cancer classification is gene selection. However, selecting a subset of genes which increases the classification accuracy is an NP-Hard problem. A variety of algorithms were proposed for gene selection in cancer classification in previous studies. In this study, a hybrid meta-heuristic algorithm, which is an integration of Genetic Algorithm and Learning Automata (GALA), is proposed for this purpose. The time complexity of GALA is O(G.m.n3) and it has acceptable accuracy and performance on some well-known cancer datasets. To evaluate the performance of GALA, six different cancer datasets including Colon, ALL_AML, SRBCT, MLL, Tumors_9 and Tumors_11 were selected. Based on the evaluation process, the GALA algorithm provided remarkable results on each dataset compared to some recently proposed algorithms.