A Fuzzy Adaptive Differential Evolution Algorithm
Soft Computing - A Fusion of Foundations, Methodologies and Applications
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Adaptive primal-dual genetic algorithms in dynamic environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A novel set-based particle swarm optimization method for discrete optimization problems
IEEE Transactions on Evolutionary Computation
Evolutionary algorithms for real world applications [Application Notes]
IEEE Computational Intelligence Magazine
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms
IEEE Transactions on Evolutionary Computation
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The control parameters in evolutionary algorithms (EAs) have significant effects on the behavior and performance of the algorithm. Most existing parameter control mechanisms are based on either individual fitness or positional distribution of population. This paper proposes a parameter adaptation strategy which aims at evaluating the density distribution of population as well as both the fitness values comprehensively, and adapting the parameters accordingly. The proposed method partitions the individuals into clusters according to their positional distribution. In order to depict the density distribution of population, a variable termed relative cluster density is proposed. Rules are used to modify the values of px and pm based on the relative cluster density and the relative sizes of clusters containing the best and the worst individuals. Experiments are conducted on a set of benchmark functions to investigate the performance and behavior of the proposed method, and the results show that the strategy is promising.