Abstract
The chaotic initialization and chaotic search are introduced into Clonal Selection Algorithm (CSA) to overcome random antibody initialization and premature convergence problems in traditional CSA. Taking full advantages of the ergodic and stochastic properties of chaotic variables, antibodies with different affinity perform chaotic search to exploit local solution space. Experimental results on test functions demonstrate that the chaotic CSA outperforms the classical clonal selection algorithm.
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