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Differential Evolution (DE) is one of the most successful and powerfulevolutionary algorithms for global optimization problem. The most importantoperator in this algorithm is mutation operator which parents are selectedrandomly to participate in it. Recently, numerous papers are tried to make thisoperator more intelligent by selection of parents for mutation intelligently.The intelligent selection for mutation vectors is performed by applying designspace (also known as decision space) criterion or fitness space criterion,however, in both cases, half of valuable information of the problem space isdisregarded. In this article, a Universal Differential Evolution (UDE) isproposed which takes advantage of both design and fitness spaces criteria forintelligent selection of mutation vectors. The experimental analysis on UDE areperformed on CEC2005 benchmarks and the results stated that UDE significantlyimproved the performance of differential evolution in comparison with othermethods that only use one criterion for intelligent selection.

A novel mutation operator based on the union of fitness and design spaces information for Differential Evolution