Gene ordering in microarray data using parallel memetic algorithms

This paper addresses the microarray gene ordering problem. It consists in ordering a set of genes, grouping together the ones with similar behavior. This behavior can be measured as the gene's activity level across a number of measurements. The gene ordering problem belongs to the NP-hard class and has strong implications in genetic and medical areas. The method employed is a memetic algorithm, which is a variant of the well known genetic algorithms. The algorithm employs several features like population structure, problem-specific crossover and mutation operators, local search, and parallel processing. The instances utilized are extracted from the literature and represent real systems with 106 up to 979 genes. The algorithm has a superior performance, successfully grouping the genes. Moreover, in this paper we evaluate the impact of parallel processing in the performance of the algorithm, especially for the larger instances, which required more computational effort.