Diversity Guided Evolutionary Programming: A novel approach for continuous optimization

Avoiding premature convergence to local optima and rapid convergence towards global optima has been the major concern with evolutionary systems research. In order to avoid premature convergence, sufficient amount of genetic diversity within the evolving population is considered necessary. Several studies have focused to devise techniques to control and preserve population diversity throughout the evolution. Since mutation is the major operator in many evolutionary systems, such as evolutionary programming and evolutionary strategies, a significant amount of research has also been done for the elegant control and adaptation of the mutation step size that is proper for traversing across the locally optimum points and reach for the global optima. This paper introduces Diversity Guided Evolutionary Programming, a novel approach to combine the best of both these research directions. This scheme incorporates diversity guided mutation, an innovative mutation scheme that guides the mutation step size using the population diversity information. It also takes some extra diversity preservative measures to maintain adequate amount of population diversity in order to assist the proposed mutation scheme. An extensive simulation has been done on a wide range of benchmark numeric optimization problems and the results have been compared with a number of recent evolutionary systems. Experimental results show that the performance of the proposed system is often better than most other algorithms in comparison on most of the problems.

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► Introduces DGEP: Diversity Guided Evolutionary Programming.
► DGEP incorporates a novel composite mutation scheme that is named as Diversity Guided Mutation (DGM).
► DGM controls mutation step size using population diversity existing at two different granularities: micro and macro.
► DGEP also incorporates some basic techniques to preserve the population diversity.
► DGEP is evaluated on 23 standard benchmark functions and 11 hybrid composition functions for numeric optimization.