Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dawkins, R.: The Blind Watchmaker, Norton (1996)
Cochrane, E.: Viva Lamarck: A Brief History of the Inheritance of Acquired Characteristics (1997)
Gould, S.J.: The panda’s Thumb. New York, Norton (1980)
Ross, B.J.: A Lamarckian Evolution Strategy for Genetic Algorithms. In: Chambers, L. (ed.) Practical Handbook of Genetic Algorithm, vol. 3, ch. 1, pp. 1–16. CRC Press, Boca Raton (1999)
Mitchell, T.M.: Machine Learning. McGraw Hill, New York (1997)
Hart, W.E., Belew, R.K.: Optimization with genetic algorithms hybrids that use local search. In: Belew, R.K., Mitchell, M. (eds.) Adaptive Individuals in Evolving Populations: Models and Algorithms. ch. 27, SFI Studies in the Sciences of Complexity, vol. 26, pp. 483–496 (1996)
Julstrom, B.A.: Comparing Darwian, Baldwinian, and Lamarckian Search in a Genetic Algorithm for the 4-Cycle Problem. Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference (1999)
Whitley, D., Gordon, V.S., Mathias, K.: Lamarckian Evolution, the Baldwin Effect and Function Optimization. In: Proceeding of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature, pp. 6–15 (1994)
Grefenstette, J.J.: Lamarckian Learning in Multi-agent Environments. In: Proceedings of the 4th International Conference on Genetic Algorithms, pp. 303–310 (1991)
Lamma, E., Riguzzi, F., Pereira, L.M.: Belief Revision via Lamarckian Evolution. Technical Report DEIS-LIA-00-004, University of Bologna, LIA Series. Italy, No.44 (2000)
Yoshii, S., Suzuki, K., Kakazu, Y.: Lamarckian GA with Genetic Supervision. In: ICEC 1995 (1995)
Du, H.F., Jiao, L.C., Wang, S.A.: Clonal Operator and Antibody Clone Algorithms. In: Shichao, Z., Qiang, Y., Chengqi, Z. (eds.) Proceedings of the First International Conference on Machine Learning and Cybernetics, pp. 506–510. IEEE, Beijing (2002)
Jiao, L.C., Wang, L.: A Novel Genetic Algorithm based on Immunity. IEEE Trans. Systems, Man and Cybernetics, Part A 30(5), 552–561 (2000)
Liang, K.H., Yao, X., Newton, C.: Evolutionary Search of Approximated N-Dimensional Landscapes. International Journal of Knowledge-Based Intelligent Engineering Systems 4(5), 172–183 (2000)
Syrjakow, M., Szczerbicka, H.: Efficient parameter optimization based on combination of direct global and local search methods. In: Davis, L.D., Jong, K.D., Vose, M.D., Whitley, D. (eds.) Evolutionary Algorithms, IMA program on mathematics in high-performance computing, pp. 227–249. Springer, New York (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
He, W., Du, H., Jiao, L., Li, J. (2005). Lamarckian Clonal Selection Algorithm Based Function Optimization. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_12
Download citation
DOI: https://doi.org/10.1007/11494669_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
Online ISBN: 978-3-540-32106-4
eBook Packages: Computer ScienceComputer Science (R0)