A new hybrid Self Organizing Migrating Genetic Algorithm for function optimization

This paper presents a new Self Organizing Migrating Genetic Algorithm (SOMGA) for function optimization, which is inspired by the features of Self Organizing Migrating Algorithm (SOMA). The uniqueness of this algorithm is that it is hybridization of binary coded GA and real coded SOMA. We compare its performance to Simple Genetic Algorithm (GA) and SOMA on 25 test functions. This algorithm is shown to be far more robust than GA and SOMA, providing fast convergence across a broad range of parameter settings.