Abstract
Several Differential Evolution variants with modified search dynamics have been recently proposed, to improve the performance of the method. This work borrows ideas from adaptive filter theory to develop an “online” algorithmic adaptation framework. The proposed framework is based on tracking the parameters of a multinomial distribution to reflect changes in the evolutionary process. As such, we design a multinomial distribution tracker to capture the successful evolution movements of three Differential Evolution algorithms, in an attempt to aggregate their characteristics and their search dynamics. Experimental results on ten benchmark functions and comparisons with five state-of-the-art algorithms indicate that the proposed framework is competitive and very promising.
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
Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V.: Self-Adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation 10(6), 646–657 (2006)
Das, S., Abraham, A., Chakraborty, U.K., Konar, A.: Differential evolution using a Neighborhood-Based mutation operator. IEEE Transactions on Evolutionary Computation 13(3), 526–553 (2009)
Das, S., Suganthan, P.N.: Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation 15(1), 4–31 (2011)
Epitropakis, M.G., Tasoulis, D.K., Pavlidis, N.G., Plagianakos, V.P., Vrahatis, M.N.: Enhancing differential evolution utilizing proximity-based mutation operators. IEEE Transactions on Evolutionary Computation 15(1), 99–119 (2011)
Gong, W., Fialho, Á., Cai, Z., Li, H.: Adaptive strategy selection in differential evolution for numerical optimization: An empirical study. Information Sciences 181(24), 5364–5386 (2011)
Lozano, M., Molina, D., Herrera, F.: Editorial scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems. Soft Computing - A Fusion of Foundations, Methodologies and Applications 15(11), 2085–2087 (2011)
Niedzwiecki, M.: Identification of Time-varying Processes. John Wiley & Sons, New York (2000)
Pavlidis, N.G., Tasoulis, D.K., Adams, N.M., Hand, D.J.: λ-perceptron: An adaptive classifier for data streams. Pattern Recognition 44(1), 78–96 (2011)
Price, K., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Natural Computing Series. Springer-Verlag New York, Inc., Secaucus (2005)
Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Transactions on Evolutionary Computation 13(2), 398–417 (2009)
Rahnamayan, S., Tizhoosh, H.R., Salama, M.M.A.: Opposition-Based differential evolution. IEEE Transactions on Evolutionary Computation 12(1), 64–79 (2008)
Storn, R., Price, K.: Differential evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces. Journal of Global Optimization 11, 341–359 (1997)
Tang, K., et al.: Benchmark functions for the CEC 2008 special session and competition on large scale global optimization. Tech. rep., Nature Inspired Computation and Applications Laboratory, USTC, China (2007)
Zhang, J., Sanderson, A.C.: JADE: adaptive differential evolution with optional external archive. IEEE Transactions on Evolutionary Computation 13(5), 945–958 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Epitropakis, M.G., Tasoulis, D.K., Pavlidis, N.G., Plagianakos, V.P., Vrahatis, M.N. (2012). Tracking Differential Evolution Algorithms: An Adaptive Approach through Multinomial Distribution Tracking with Exponential Forgetting. In: Maglogiannis, I., Plagianakos, V., Vlahavas, I. (eds) Artificial Intelligence: Theories and Applications. SETN 2012. Lecture Notes in Computer Science(), vol 7297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30448-4_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-30448-4_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30447-7
Online ISBN: 978-3-642-30448-4
eBook Packages: Computer ScienceComputer Science (R0)