Abstract
In this paper, Immune Clonal Selection theory and Lamarckism are integrated to form a new algorithm, Lamarckian Polyclonal Programming Algorithm (LPPA), for solving the global numerical optimization problem. 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 Adaptive Polyclonal Programming Algorithm (APPA). In the experiments, ten benchmark functions are used to test the performance of LPPA, and the scalability of LPPA along the problem dimension is studied with great care. The results show that LPPA achieves a good performance when the dimensions are increased from 20-10,000. Moreover, even when the dimensions are increased to as high as 10,000, LPPA still can find high quality solutions at a low computation cost. Therefore, LPPA has good scalability and is a competent algorithm for solving high dimensional optimization problems.
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He, W., Du, H., Jiao, L., Li, J. (2005). Lamarckian Polyclonal Programming Algorithm for Global Numerical Optimization. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_130
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DOI: https://doi.org/10.1007/11539117_130
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
Online ISBN: 978-3-540-31858-3
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