  Differential Evolution (DE) is one of the most successful and powerful
evolutionary algorithms for global optimization problem. The most important
operator in this algorithm is mutation operator which parents are selected
randomly to participate in it. Recently, numerous papers are tried to make this
operator more intelligent by selection of parents for mutation intelligently.
The intelligent selection for mutation vectors is performed by applying design
space (also known as decision space) criterion or fitness space criterion,
however, in both cases, half of valuable information of the problem space is
disregarded. In this article, a Universal Differential Evolution (UDE) is
proposed which takes advantage of both design and fitness spaces criteria for
intelligent selection of mutation vectors. The experimental analysis on UDE are
performed on CEC2005 benchmarks and the results stated that UDE significantly
improved the performance of differential evolution in comparison with other
methods that only use one criterion for intelligent selection.
