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. © 2008 Springer Berlin Heidelberg.
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CITATION STYLE
Yang, M., & Guan, J. (2008). Dynamic clonal and chaos-mutation evolutionary algorithm for function optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5370 LNCS, pp. 19–27). https://doi.org/10.1007/978-3-540-92137-0_3
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