A dual mutation strategy embedded Evolutionary Programming for continuous optimization

Evolutionary Programming (EP) and Differential Evolution (DE) are well known as simple and efficient schemes for global optimization over continuous spaces. Both EP and DE use mutation for producing offspring. The mutation operators of EP usually generate the search step size for mutation by probability distribution functions while the mutation operators of DE generate it by adding a weighted difference vector between two individuals to a third individual. In this paper, a new EP algorithm is proposed based on dual mutation strategy (DMEP) as it incorporates both the mutation operators of EP and DE literature. Thus the balance between exploration and exploitation is obtained by two different categories of mutation operators. To evaluate the performance of the proposed scheme, a test-suite of 37 benchmark functions has been used and results have been compared with some prominent evolutionary systems. Experimental results show the remarkable effectiveness of the dual mutation strategy employed by DMEP.