Memetic and opposition-based learning genetic algorithms for sorting unsigned genomes by translocations

Abstract

A standard genetic algorithm (GAS) for sorting unsigned genomes by translocations is improved in two different manners: firstly, a memetic algorithm (GAM) is provided, which embeds a newstage of local search, based on the concept of mutation applied in only one gene; secondly, an opposition-based learning (GAOBL) mechanism is provided that explores the concept of internal opposition applied to a chromosome. Both approaches include a convergence control mechanism of the population using the Shannon entropy. For the experiments, both biological and synthetic genomes were used. The results showed that GAMoutperforms both GASand GAOBLas confirmed through statistical tests.