An enhance DE algorithm for analysis in dataset

Differential evolution (DE) is a simple, powerful optimisation algorithm widely used to solve constrained optimisation problems, multi objective global optimisations and complex real-world applications. However, the choices of the best mutation, search strategies, long training time and lower classification accuracy are difficult for authors to know the appropriate encoding schemes and evolutionary operators. Otherwise, it may be lead to demanding computational costs of the time consuming trial and error parameter and operator tuning process. Moreover, mutation and crossover plays an important role in the DE optimisation and several functions are available for it may leads a different result for the same dataset. Therefore, an enhance DE has been proposed to improve searching ability, crossover and mutation strategy to efficiently guide the evolution of the population towards the global optimum in less time.