Information Sciences: an International Journal
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Differential Evolution (DE) is a powerful optimization procedure that self-adapts to the search space, although DE lacks diversity and sufficient bias in the mutation step to make efficient progress on non-separable problems. We present an enhancement to DE that introduces greater diversity while also directing the search to more promising regions. The Combinatorial Sampling Differential Evolution (CSDE) is introduced which can sample vectors in two ways; highly correlated with the search space or around a ‘better’ individual. The CSDE approach can provide a similar number of samples as crossover, without being biased towards the principle coordinate axes of a decision space. This approach to sampling vectors is capable of optimizing problems with extensive parameter interactions. It also demonstrates fast convergence towards the global optimum and is highly scalable in the decision space on a variety of single and multi-objective problems due to the balance between sampling highly directed correlated vectors and non-correlated vectors which contribute to sampling diversity.