Regular PapersNo Access

LAMARCKIAN LEARNING IN CLONAL SELECTION ALGORITHM FOR NUMERICAL OPTIMIZATION

Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China, Institute of Intelligent Information Processing, Xidian University, No. 2 South TaiBai Road, Xi'an, Shaanxi 710071, China

Search for more papers by this author

LICHENG JIAO

Search for more papers by this author

JIE YANG

Search for more papers by this author

, and

FANG LIU

Search for more papers by this author

https://doi.org/10.1142/S0218213010000029Cited by:8 (Source: Crossref)

Abstract

In this paper, we introduce Lamarckian learning theory into the Clonal Selection Algorithm and propose a sort of Lamarckian Clonal Selection Algorithm, termed as LCSA. The major aim is to utilize effectively the information of each individual to reinforce the exploitation with the help of Lamarckian local search. Recombination operator and tournament selection operator are incorporated into LCSA to further enhance the ability of global exploration. We compare LCSA with the Clonal Selection Algorithm in solving twenty benchmark problems to evaluate the performance of LCSA. The results demonstrate that the Lamarckian local search makes LCSA more effective and efficient in solving numerical optimization problems.

Keywords:

Remember to check out the Most Cited Articles!
Check out Notable Titles in Artificial Intelligence.

References

L. N. de Castro and J. Timmis , Artificial Immune Systems: A New Computational Intelligence Approach ( Springer-Verlag , New York , 2002 ) . Google Scholar
E. Hart and J. Timmis, Application Areas of AIS: The Past, The Present and The Future, Proceedings of the Fourth International Conference on Artificial Immune Systems, ICARIS 20053627, Lecture Notes in Computer Science (2005) pp. 483–497. Google Scholar
V. Vassiliadis and G. Dounias, International Journal on Artificial Intelligence Tools 18(4), 487 (2009). Link, Web of Science, Google Scholar
L. N. de Castro and F. J. Von Zuben, IEEE Transactions on Evolutionary Computation 6(3), 239 (2002), DOI: 10.1109/TEVC.2002.1011539. Crossref, Web of Science, Google Scholar
J. Kelsey and J. Timmis, Immune inspired somatic contiguous hypermutation for function optimisation, Proceedings of the Genetic and Evolutionary Computation Conference (Springer, Berlin, 2003) pp. 207–218. Google Scholar
V. Cutello, G. Nicosia and M. Pavone, Exploring the Capability of Immune Algorithms: A Characterization of Hypemutation Operators, Proceedings of Third International Conference on Artificial Immune Systems3239, Lecture Notes in Computer Science (2004) pp. 263–276. Google Scholar
C. A. Coello Coello and N. C. Cortes, Genetic Programming and Evolvable Machines 6, 163 (2005). Crossref, Google Scholar
M. Gonget al., Evolutionary Computation 16(2), 225 (2008), DOI: 10.1162/evco.2008.16.2.225. Crossref, Web of Science, Google Scholar
M. Gonget al., Science in China Series F: Information Sciences 51(8), 1064 (2008), DOI: 10.1007/s11432-008-0040-2. Crossref, Google Scholar
M. Gong, L. Jiao and X. Zhang, Neurocomputing 72(1-3), 149 (2008), DOI: 10.1016/j.neucom.2007.12.041. Crossref, Web of Science, Google Scholar
M. Gonget al., Progress in natural science, Elsevier 19(2), 237 (2009), DOI: 10.1016/j.pnsc.2008.05.026. Crossref, Google Scholar
J. Yenet al., IEEE Transactions on Systems, Man and Cybernetics - Part B 28, 173 (1998), DOI: 10.1109/3477.662758. Crossref, Web of Science, Google Scholar
R. Salomon, IEEE Transactions on Evolutionary Computation 2, 45 (1998), DOI: 10.1109/4235.728207. Crossref, Google Scholar
Y. S. Ong and A. J. Keane, IEEE Transactions on Evolutionary Computation 8(2), 99 (2004), DOI: 10.1109/TEVC.2003.819944. Crossref, Web of Science, Google Scholar
N. Krasnogor and J. E. Smith, IEEE transactions on Evolutionary Computation 9(5), 474 (2005), DOI: 10.1109/TEVC.2005.850260. Crossref, Web of Science, Google Scholar
Y. S. Onget al., IEEE Transactions on Systems, Man and Cybernetics – Part B 36(1), 141 (2006). Web of Science, Google Scholar
Z. Z. Zhouet al., IEEE Transactions on Systems, Man and Cybernetics – Part C 37(1), 66 (2007), DOI: 10.1109/TSMCC.2005.855506. Crossref, Web of Science, Google Scholar
P. Gancarski and A. Blansche, IEEE Transactions on Evolutionary Computation 12(5), 617 (2008), DOI: 10.1109/TEVC.2008.920670. Crossref, Web of Science, Google Scholar
V. Tirronenet al., Evolutionary Computation 16(4), 529 (2008), DOI: 10.1162/evco.2008.16.4.529. Crossref, Web of Science, Google Scholar
C.-C. Peng and G. D. Magoulas, International Journal of Artificial Intelligence Tools 17(5), 963 (2008). Link, Google Scholar
I. Devarenne, H. Mabed and A. Caminada, International Journal of Artificial Intelligence Tools 17(1), 195 (2008). Link, Google Scholar
Q. H. Nguyenet al., Evolutionary Computation 17(2), 231 (2009). Web of Science, Google Scholar
Q. C. Nguyen, Y. S. Ong and M. H. Lim, IEEE Transactions on Evolutionary Computation 13(3), 604 (2009), DOI: 10.1109/TEVC.2008.2009460. Crossref, Web of Science, Google Scholar
F. M. Burnet , The Clonal Selection Theory of Acquired Immunity ( Cambridge University Press , Cambridge , 1959 ) . Crossref, Google Scholar
G. L. Ada and G. J. V. Nossal, Scientific American 257(2), 50 (1987), DOI: 10.1038/scientificamerican0887-62. Google Scholar
J. Baker, Adaptive Selection Methods for Genetic Algorithms, Proceedings of Second International Conference on Genetic Algorithms (Lawrence Erlbaum Associates, Hillsdale, 1985) pp. 100–111. Google Scholar
M. J. D. Powell, Acta Numerica 7, 287 (1998), DOI: 10.1017/S0962492900002841. Crossref, Google Scholar
R. Swinburne , Bayes's Theorem ( Oxford University Press , Oxford , 2002 ) . Google Scholar
K. Deb and H. G. Beyer, Evolutionary Computation 9(2), 197 (2001), DOI: 10.1162/106365601750190406. Crossref, Web of Science, Google Scholar
R. McGill, J. W. Tukey and W. A. Larsen, The American Statistician 32, 12 (1978), DOI: 10.2307/2683468. Crossref, Web of Science, Google Scholar