Abstract
Lamarckian learning has been introduced into evolutionary computation to enhance the ability of local search. The relevant research topic, memetic computation, has received significant amount of interest. In this study, a novel memetic computational framework is proposed by simulating the integrated regulation between neural and immune systems. The Lamarckian learning strategy of simulating the unidirectional regulation of neural system on immune system is designed. Consequently, an immune memetic algorithm based on the Lamarckian learning is proposed for numerical optimization. The proposed algorithm combines the advantages of immune algorithms and mathematical programming, and performs well in both global and local search. The simulation results based on ten low-dimensional and ten high-dimensional benchmark problems show that the immune memetic algorithm outperforms the basic genetic algorithm-based memetic algorithm in solving most of the test problems.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Hart W E, Krasnogor N, Smith J E. Recent Advances in Memetic Algorithms. Berlin: Springer, 2005
Dawkins R. The Selfish Gene. New York: Oxford Univ. Press, 1976
Radcliffe N J, Surry P D. Formal memetic algorithms. In: Fogarty T C, ed. Evolutionary Computing. Berlin: Springer, 1994. 1–16
Kazarlis S A, Papadakis S E, Theocharis J B, et al. Microgenetic algorithms as generalized hill-climbing operators for GA optimization. IEEE Trans Evolut Comput, 2001, 5: 204–217
Ishibuchi H, Yoshida T, Murata T. Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Trans Evolut Comput, 2003, 7: 204–223
Ong Y S, Keane A J. Meta-Lamarckian learning in memetic algorithms. IEEE Trans Evolut Comput, 2004, 8: 99–110
Krasnogor N, Smith J. A tutorial for competent memetic algorithms: model, taxonomy, and design issues. IEEE Trans Evolut Comput, 2005, 9: 474–488
Zhu Z, Ong Y S, Dash M. Wrapper-filter feature selection algorithm using a memetic framework. IEEE Trans Syst Man Cybernet B, 2007, 37: 70–76
Tan K C, Chiam S C, Mamun A A, et al. Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization. Europ J Oper Res, 2009, 197: 701–713
Nguyen Q C, Ong Y S, Lim M H. A probabilistic memetic framework. IEEE Trans Evolut Comput, 2009, 13: 604–623
Ang J H, Tan K C, Mamun A A. An evolutionary memetic algorithm for rule extraction. Exp Syst Appl, 2010, 37: 1302–1315
Ong Y S, Lim M H, Chen X. Research frontier: towards memetic computing. IEEE Comput Intell Mag, 2010, 5: 24–31
Jiao L C, Gong M G, Wang S, et al. Natural and remote sensing image segmentation using memetic computing. IEEE Comput Intell Mag, 2010, 5: 78–91
de Castro L N, Timmis J. Artificial Immune Systems: A New Computational Intelligence Approach. Berlin, Heidelberg, New York: Springer-Verlag, 2002
Zhou J, Dasgupta D. Revisiting negative selection algorithms. Evolut Comput, 2007, 15: 223–251
de Castro L N, Von Zuben F J. Learning and optimization using the clonal selection principle. IEEE Trans Evolut Comput, 2002, 6: 239–251
Timmis J, Neal M. A resource limited artificial immune system for data analysis. Knowl Based Syst, 2001, 14: 121–130
Aickelin U, Cayzer S. The danger theory and its application to artificial immune systems. In: Proc of the International Conference on Artificial Immune Systems, Canterbury, UK, 2002
Jiao L C, Du H F, Liu F, et al. Immune Computation for Optimization, Learning and Recognition (in Chinese). Beijing: Science Press, 2006
Mo H W, Zuo X Q. Artificial Immune Systems (in Chinese). Beijing: Science Press, 2009
Timmis J. Artificial immune systems—today and tomorrow. Nat Comput, 2007, 6: 1–18
Gong M G, Du H F, Jiao L C. Optimal approximation of linear systems by artificial immune response. Sci China Ser F-Inf Sci, 2006, 49: 63–79
Gong M G, Jiao L C, Du H F, et al. Multiobjective immune algorithm with nondominated neighbor-based selection. Evolut Comput, 2008, 16: 225–255
Gong M G, Jiao L C, Ma W P, et al. Multiobjective optimization using an immunodominance and clonal selection inspired algorithm. Sci China Ser F-Inf Sci, 2008, 51: 1064–1082
Gong M G, Jiao L C, Zhang X R. A population-based artificial immune system for numerical optimization. Neurocomputing, 2008, 72: 149–161
Zuo X Q, Mo H W, Wu J P. A robust scheduling method based on a multi-objective immune algorithm. Inf Sci, 2009, 179: 3359–3369
Gong M G, Jiao L C, Ma W P, et al. Intelligent multi-user detection using an artificial immune system. Sci China Ser F-Inf Sci, 2009, 52: 2342–2353
Gong M G, Jiao L C, Zhang L N. Baldwinian learning in clonal selection algorithm for optimization. Inf Sci, 2010, 180: 1218–1236
Dustin M L, Colman D R. Neural and immunological synaptic relations. Science, 2002, 298: 785–789
Gendrel M, Rapti G, Richmond J E, et al. A secreted complement-control-related protein ensures acetylcholine receptor clustering. Nature, 2009, 461: 992–996
Styer K L, Singh V, Macosko E, et al. Innate immunity in Caenorhabditis elegans is regulated by neurons expressing NPR-1/GPCR. Science, 2008, 322: 460–464
Powell M J D. An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput J, 1964, 7: 303–307
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gong, M., Jiao, L., Liu, F. et al. Memetic computation based on regulation between neural and immune systems: the framework and a case study. Sci. China Inf. Sci. 53, 1519–1527 (2010). https://doi.org/10.1007/s11432-010-4019-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11432-010-4019-4