Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Dynamic Parameter Encoding for Genetic Algorithms
Machine Learning
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Genetic Algorithms in Search, Optimization and Machine Learning
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
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Principles and methods of artificial immune system vaccination of learning systems
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This paper presents a novel genetic clustering algorithm combining a genetic algorithm (GA) with the classical hard c-means clustering algorithm (HCMCA). It processes partition matrices rather than sets of center points and thus provides a new implementation scheme for the genetic operator - recombination. For comparison of performance with other existing clustering algorithms, a gray-level image quantization problem is considered. Experimental results show that the proposed algorithm converges more quickly to the global optimum and thus provides a better way out of the dilemma in which the traditional clustering algorithms are easily trapped in local optima and the genetic approach is time consuming.