Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Computational Optimization and Applications
Automatic kernel clustering with a Multi-Elitist Particle Swarm Optimization Algorithm
Pattern Recognition Letters
Differential evolution using a neighborhood-based mutation operator
IEEE Transactions on Evolutionary Computation
Preserving and exploiting genetic diversity in evolutionary programming algorithms
IEEE Transactions on Evolutionary Computation
The global kernel k-means algorithm for clustering in feature space
IEEE Transactions on Neural Networks
Combining mutation operators in evolutionary programming
IEEE Transactions on Evolutionary Computation
Evolutionary programming using mutations based on the Levy probability distribution
IEEE Transactions on Evolutionary Computation
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
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A hybrid memetic algorithm, called a memetic algorithm with double mutation operators (MADM), is proposed to deal with the problem of global optimization. In this paper, the algorithm combines two meta-learning systems to improve the ability of global and local exploration. The double mutation operators in our algorithms guide the local learning operator to search the global optimum; meanwhile the main aim is to use the favorable information of each individual to reinforce the exploitation with the help of two meta-learning systems. Crossover operator and elitism selection operator are incorporated into MADM to further enhance the ability of global exploration. In the first part of the experiments, six benchmark problems and six CEC2005@?s problems are used to test the performance of MADM. For the most problems, the experimental results demonstrate that MADM is more effective and efficient than other improved evolutionary algorithms for numerical optimization problems. In the second part of the experiments, MADM is applied to a practical problem, clustering complex and linearly non-separable datasets, with a satisfying result.