Multiparent recombination in evolutionary computing
Advances in evolutionary computing
Immune clonal strategies based on three mutation methods
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
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This paper proposes a novel hybrid immune clonal selection algorithm coupling with multi-parent crossover and chaos mutation (CSACC). CSACC takes advantages of the clonal selection mechanism and the learning capability of the clonal selection alorithm (CLONALG). By introducing the multi-parent crossover and neighbourhood mutation operator, CSACC achieves a dynamic balance between exploration and exploitation. And by using the characteristics of ergodicity and dynamic of chaos variables, the chaotic optimization mechanism is introduced into CLONALG to improve its search efficiency. The experimental results on function optimization show that the hybrid algorithm is more efficient than the clonal selection algorithm.