Abstract
Memetic algorithms arise as very effective algorithms to obtain reliable and high accurate solutions for complex continuous optimization problems. Nowadays, high-dimensional optimization problems are an interesting field of research. Its high dimension introduces new problems for the optimization process, making recommendable to test the behavior of optimization algorithms to large-scale problems. In memetic algorithms, the local search method is responsible of exploring the neighborhood of the current solutions; therefore, the dimensionality has a direct influence over this component. The aim of this paper is to study this influence. We design different memetic algorithms that only differ in the local search method applied, and they are compared using two sets of continuous benchmark functions: a standard one and a specific set with large-scale problems. The results show that high dimensionality reduces the differences among the different local search methods.
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
Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)
Goldberg, D.E., Voessner, S.: Optimizing global-local search hybrids. In: Banzhaf, W., et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference 1999. Morgan Kaufmann, San Mateo (1999)
Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Technical report, Technical Report Caltech Concurrent Computation Program Report 826, Caltech, Pasadena, CA (1989)
Moscato, P.: Memetic algorithms: a short introduction, pp. 219–234. McGraw-Hill, London (1999)
Merz, P.: Memetic Algorithms for Combinational Optimization Problems: Fitness Landscapes and Effective Search Strategies. PhD thesis, Gesamthochschule Siegen, University of Siegen, Germany (2000)
Hart, W.: Adaptive Global Optimization With Local Search. PhD thesis, Univ. California, San Diego, CA (1994)
Lozano, M., Herrera, F., Krasnogor, N., Molina, D.: Real-coded Memetic Algorithms with Crossover Hill-climbing. Evolutionary Computation 12(2), 273–302 (2004)
Ong, Y.S., Keane, A.J.: Meta-Lamarckian Learning in Memetic Algorithms. IEEE Transactions on Evolutionary Computation 4(2), 99–110 (2004)
Noman, N., Iba, H.: Accelerating differential evolution using an adaptive local search. IEEE Transactions on Evolutionary Computation 12(1), 107–125 (2008)
Molina, D., Lozano, M., García-Martínez, C., Herrera, F.: Memetic algorithms for continuous optimization based on local search chains. Evolutionary Computation (in press, 2009)
van den Bergh, F., Engelbrencht, A.: A cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation, 225–239 (2004)
Solis, F.J., Wets, R.J.: Minimization by Random Search Techniques. Mathematical Operations Research 6, 19–30 (1981)
Nelder, J., Mead, R.: A simplex method for functions minimizations. Computer Journal 7(4), 308–313 (1965)
Hansen, N., Ostermeier, A.: Adapting Arbitrary Normal Mutation Distributions in Evolution Strategies: The Covariance Matrix Adaptation. In: Proceeding of the IEEE International Conference on Evolutionary Computation (ICEC 1996), pp. 312–317 (1996)
Hansen, N., Müller, S., Koumoutsakos, P.: Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es). Evolutionary Computation 11(1), 1–18 (2003)
Hansen, N., Kern, S.: Evaluating the CMA evolution strategy on multimodal test functions. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 282–291. Springer, Heidelberg (2004)
Auger, A., Hansen, N.: Performance Evaluation of an Advanced Local Search Evolutionary Algorithm. In: 2005 IEEE Congress on Evolutionary Computation, pp. 1777–1784 (2005)
Auger, A., Hansen, N.: A Restart CMA Evolution Strategy with Increasing Population Size. In: 2005 IEEE Congress on Evolutionary Computation, pp. 1769–1776 (2005)
Suganthan, P., Hansen, N., Liang, J., Deb, K., Chen, Y., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the CEC 2005 special session on real parameter optimization. Technical report, Nanyang Technical University (2005)
Sywerda, G.: Uniform crossover in genetic algorithms. In: Schaffer, J. (ed.) Proc. of the International Conference on Genetic Algorithms, pp. 2–9 (1989)
Land, M.S.: Evolutionary Algorithms with Local Search for Combinational Optimization. PhD thesis, Univ. California, San Diego, CA (1998)
Herrera, F., Lozano, M., Verdegay, J.L.: Tackling Real-coded Genetic Algorithms: Operators and Tools for the Behavioral Analysis. Artificial Intelligence Reviews 12(4), 265–319 (1998)
Fernandes, C., Rosa, A.: A Study of non-Random Matching and Varying Population Size in Genetic Algorithm using a Royal Road Function. In: Proc. of the 2001 Congress on Evolutionary Computation, pp. 60–66 (2001)
García, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: A case study on the cec 2005 special session on real parameter optimization. Journal of Heuristics (2008) (in press)
Hansen, N.: Compilation of Results on the CEC Benchmark Function Set. Technical report, Institute of Computational Science, ETH Zurich, Switerland (2005), http://www.ntu.edu.sg/home/epnsugan/index_files/CEC-05/compareresults.pdf
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Molina, D., Lozano, M., Herrera, F. (2009). Study of the Influence of the Local Search Method in Memetic Algorithms for Large Scale Continuous Optimization Problems. In: Stützle, T. (eds) Learning and Intelligent Optimization. LION 2009. Lecture Notes in Computer Science, vol 5851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11169-3_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-11169-3_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11168-6
Online ISBN: 978-3-642-11169-3
eBook Packages: Computer ScienceComputer Science (R0)