An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Differential evolution using a neighborhood-based mutation operator
IEEE Transactions on Evolutionary Computation
Diversity Guided Evolutionary Programming: A novel approach for continuous optimization
Applied Soft Computing
Combining mutation operators in evolutionary programming
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Where Are the Niches? Dynamic Fitness Sharing
IEEE Transactions on Evolutionary Computation
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Evolutionary algorithms often suffer from premature convergence when dealing with complex multi-modal function optimization problems as the fitness landscape may contain numerous local optima. To avoid premature convergence, sufficient amount of genetic diversity within the evolving population needs to be preserved. In this paper we investigate the impact of two different categories of mutation operators on evolutionary programming in an attempt to preserve genetic diversity. Participation of the mutation operators on the evolutionary process is guided by fitness stagnation and localization information of the individuals. A simple experimental analysis has been shown to demonstrate the effectiveness of the proposed scheme over a test-suite of five classical benchmark functions