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Lamarckian Clonal Selection Algorithm Based Function Optimization

机译:基于拉马克克隆选择算法的功能优化

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Based on Lamarckism and Immune Clonal Selection Theory, Lamarckian Clonal Selection Algorithm (LCSA) is proposed in this paper. In the novel algorithm, the idea that Lamarckian evolution described how organism can evolve through learning, namely the point of "Gain and Convey" is applied, then this kind of learning mechanism is introduced into Standard Clonal Selection Algorithm (SCSA). Through the experimental results of optimizing complex multimodal functions, compared with SCSA and the relevant evolutionary algorithm, LCSA is more robust and has better convergence.
机译:基于拉马基和免疫克隆选择理论,本文提出了拉马克克隆选择算法(LCSA)。在小说算法中,Lamarckian进化描述了有机体如何通过学习演变的想法,即应用了“增益和传达”的点,然后将这种学习机制引入标准克隆选择算法(SCSA)。通过优化复杂多模函数的实验结果,与SCSA和相关的进化算法相比,LCSA更加坚固并且具有更好的收敛性。

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