Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
An Analysis of Evolutionary Algorithms Based on Neighborhood and Step Sizes
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
An Experimental Investigation of Self-Adaptation in Evolutionary Programming
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Solving Cutting Stock Problems by Evolutionary Programming
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Toward a theory of evolution strategies: Self-adaptation
Evolutionary Computation
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Evolutionary programming (EP) has been widely used in numerical optimization in recent years. The adaptive parameters, also named step size control, in EP play a significant role which controls the step size of the objective variables in the evolutionary process. However, the step size control may not work in some cases. They are frequently lost and then make the search stagnate early. Applying the lower bound can maintain the step size in a work range, but it also constrains the objective variables from being further explored. In this paper, an adaptively adjusted lower bound is proposed which supports better fine-tune searches and spreads out exploration as well.