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
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Accelerating real-valued genetic algorithms using mutation-with-momentum
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Directed variation in evolution strategies
IEEE Transactions on Evolutionary Computation
Adaptive directed mutation for real-coded genetic algorithms
Applied Soft Computing
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Developing directed mutation methods has been an interesting research topic to improve the performance of genetic algorithms (GAs) for function optimization. This paper introduces a directed mutation (DM) operator for GAs to explore promising areas in the search space. In this DM method, the statistics information regarding the fitness and distribution of individuals over intervals of each dimension is calculated according to the current population and is used to guide the mutation of an individual toward the neighboring interval that has the best statistics result in each dimension. Experiments are carried out to compare the proposed DM technique with an existing directed variation on a set of benchmark test problems. The experimental results show that the proposed DM operator achieves a better performance than the directed variation on most test problems.