A Cultivated Differential Evolution Algorithm using modified Mutation and Selection Strategy

In the present study a modified new variant of Differential Evolution (DE) is proposed, named Cultivated Differential Evolution (CuDE). This algorithm is different from basic DE in two ways. Firstly, the selection of the base vector for mutation operation is not totally randomized while in basic DE uniformly generated random numbers serve this task. Secondly, information preservation concept is used to generate population for the next generation. The performance of the proposed algorithm is validated on a bed of eight benchmark problems taken from literature and compared against the basic DE and some other variants of DE. The numerical results show that the proposed algorithm helps in formulating a better trade-off between convergence rate and efficiency. Keywords: Differential Evolution, Mutation, Selection, Information Preservation, Population Segmentation