Abstract
This paper introduced a dynamic-clone and chaos-mutation evolutionary algorithm (DCCM-EA), which employs dynamic clone and chaos mutation methods, for function optimization. The number of clone is direct proportion to “affinity” between individuals and the chaos sequence can search the points all over the solution space, so DCCM-EA can make all points get equal evolutionary probability, to get the global optimal solution most possibly. In the experiments, taking 23 benchmark functions to test, it can be seen that DCCM-EA if effective for solving function optimization.
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Yang, M., Guan, J. (2008). Dynamic Clonal and Chaos-Mutation Evolutionary Algorithm for Function Optimization. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2008. Lecture Notes in Computer Science, vol 5370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92137-0_3
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DOI: https://doi.org/10.1007/978-3-540-92137-0_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-92136-3
Online ISBN: 978-3-540-92137-0
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