Abstract
Numerical optimization of given objective functions is a crucial task in many real-life problems. The present article introduces an immunological algorithm for continuous global optimization problems, called opt-IA. Several biologically inspired algorithms have been designed during the last few years and have shown to have very good performance on standard test bed for numerical optimization.
In this paper we assess and evaluate the performance of opt-IA, FEP, IFEP, DIRECT, CEP, PSO, and EO with respect to their general applicability as numerical optimization algorithms. The experimental protocol has been performed on a suite of 23 widely used benchmarks problems. The experimental results show that opt-IA is a suitable numerical optimization technique that, in terms of accuracy, generally outperforms the other algorithms analyzed in this comparative study. The opt-IA is also shown to be able to solve large-scale problems.
This is a preview of subscription content, log in via an institution to check access.
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
Nicosia, G.: Immune Algorithms for Optimization and Protein Structure Prediction., Ph.D. Thesis, University of Catania, Italy (December 2004)
De Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Paradigm. Springer, London (2002)
Cutello, V., Nicosia, G.: The Clonal Selection Principle for in silico and in vitro Computing. In: de Castro, L.N., Von Zuben, F.J. (eds.) Recent Developments in Biologically Inspired Computing (2004)
Cutello, V., Nicosia, G., Pavone, M.: Exploring the capability of immune algorithms: A characterization of hypermutation operators. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 263–276. Springer, Heidelberg (2004)
Nicosia, G., Cutello, V., Pavone, M.: An Immune Algorithm with Hyper-Macromutations for the Dill’s 2D Hydrophobic-Hydrophilic Model. In: Congress on Evolutionary Computation, CEC 2004, vol. 1, pp. 1074–1080. IEEE Press, Los Alamitos (2004)
Cutello, V., Morelli, G., Nicosia, G., Pavone, M.: Immune Algorithms with Aging operators for the String Folding Problem and the Protein Folding Problem. In: Raidl, G.R., Gottlieb, J. (eds.) EvoCOP 2005. LNCS, vol. 3448, pp. 80–90. Springer, Heidelberg (2005)
Nicosia, G., Cutello, V., Pavone, M.: A Hybrid Immune Algorithm with Information Gain for the Graph Coloring Problem. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 171–182. Springer, Heidelberg (2003)
Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.): ICARIS 2004. LNCS, vol. 3239. Springer, Heidelberg (2004)
Goldberg, D.E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms, vol. 7. Kluwer Academic Publisher, Boston (2002)
De Castro, L.N., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Trans. on Evolutionary Computation 6(3), 239–251 (2002)
De Castro, L.N., Timmis, J.: An Artificial Immune Network for Multimodal Function Optimization. In: CEC 2002, Proceeding of IEEE Congress on Evolutionary Computation, IEEE Press, Los Alamitos (2002)
Yao, X., Liu, Y., Lin, G.M.: Evolutionary programming made faster. IEEE Trans. on Evolutionary Computation 3, 82–102 (1999)
Chellapilla, K.: Combining mutation operators in evolutionary programming. IEEE Trans. Evol. Comput. 2, 91–96 (1998)
Angeline, P.J.: Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences. In: Porto, V.W., Saravanan, N., Waagen, D., Eiben, A.E. (eds.) Proc. Evolutionary Programming VII, pp. 601–610 (1998)
Jones, D.R., Perttunen, C.D., Stuckman, B.E.: Lipschitzian optimization without the lipschitz constant. J. of Optimization Theory and Application 79, 157–181 (1993)
Finkel, D.E.: DIRECT Optimization Algorithm User Guide. Technical Report, CRSC N.C. State University (March 2003), ftp://ftp.ncsu.edu/pub/ncsu/crsc/pdf/crsc-tr03-11.pdf
Timmis, J., Kelsey, J.: Immune Inspired Somatic Contiguous Hypermutation for Function Optimisation. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 207–218. Springer, Heidelberg (2003)
Vesterstrom, J., Thomsen, R.: A Comparative Study of Differential Evolution, Particle Swarm Optimization, and Evolutionary Algorithms on Numerical Benchmark Problems. In: Congress on Evolutionary Computation, CEC 2004, vol. 2, pp. 1980–1987. IEEE Press, Los Alamitos (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cutello, V., Narzisi, G., Nicosia, G., Pavone, M. (2006). An Immunological Algorithm for Global Numerical Optimization. In: Talbi, EG., Liardet, P., Collet, P., Lutton, E., Schoenauer, M. (eds) Artificial Evolution. EA 2005. Lecture Notes in Computer Science, vol 3871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11740698_25
Download citation
DOI: https://doi.org/10.1007/11740698_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33589-4
Online ISBN: 978-3-540-33590-0
eBook Packages: Computer ScienceComputer Science (R0)