The hybridization and combination of different Evolutionary Algorithms to improve the quality of the solutions and to accelerate execution is a common research practice. In this paper, we utilize Genetic Programming to evolve novel Differential Evolution operators. The genetic evolution resulted in parameter free Differential Evolution operators. Our experimental results indicate that the performance of the genetically programmed operators is comparable and in some cases is considerably better than the already existing human designed ones.
['Nicos G. Pavlidis', 'Vassilis P. Plagianakos', 'Dimitris K. Tasoulis', 'Michael N. Vrahatis']
Human Designed Vs. Genetically Programmed Differential Evolution Operators