Abstract
Local search algorithms operating in high-dimensional and multimodal search spaces often suffer from getting trapped in a local optima, therefore requiring many restarts. Even with multiple restarts, their search efficiency critically depends on the choice of the neighborhood structure. In this paper we propose an approach in which the need for the restarts is exploited to improve the neighborhood definitions. Namely, a graph clustering based linkage detection method is used to mine the information from several runs, in order to extract variable dependencies and update the neighborhood structure, variation operators accordingly. We show that the adaptive neighborhood structure approach enables the efficient solving of challenging global optimization problems that are both deceptive and multimodal.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Pelikan, M.: Hierarchical Bayesian optimization algorithm: Toward a new generation of evolutionary algorithms. Springer (2005)
Goldberg, D.E.: The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers, Norwell (2002)
Goldberg, D.E., Deb, K., Kargupta, H., Harik, G.: Rapid, accurate optimization of difficult problems using fast messy genetic algorithms. In: Proceedings of the Fifth International Conference on Genetic Algorithms, San Mateo, CA, pp. 56–64. Morgan Kaufman (1993)
Harik, G.R., Goldberg, D.E.: Learning linkage. In: Belew, R.K., Vose, M.D. (eds.) FOGA, pp. 247–262. Morgan Kaufmann (1996)
Harik, G.R., Lobo, F.G., Goldberg, D.E.: The compact genetic algorithm. IEEE-EC 3(4), 287 (1999)
Pelikan, M., Goldberg, D.E., Cantú-Paz, E.: BOA: The Bayesian optimization algorithm. In: Banzhaf, W., et al. (eds.) GECCO 1999, Orlando, FL, July 13-17, vol. I, pp. 525–532. Morgan Kaufmann Publishers, San Fransisco (1999)
Watson, R.A., Pollack, J.: A computational model of symbiotic composition in evolutionary transitions. Biosystems 69(2-3), 187–209 (2003), Special Issue on Evolvability, ed. Nehaniv
de Jong, E.D.: Representation Development from Pareto-Coevolution. In: Cantú-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 262–273. Springer, Heidelberg (2003)
Toussaint, M.: Compact Genetic Codes as a Search Strategy of Evolutionary Processes. In: Wright, A.H., Vose, M.D., De Jong, K.A., Schmitt, L.M. (eds.) FOGA 2005. LNCS, vol. 3469, pp. 75–94. Springer, Heidelberg (2005)
de Jong, E.D., Thierens, D., Watson, R.A.: Hierarchical genetic algorithms. In: Yao, X., et al. (eds.) PPSN VIII. LNCS, vol. 3242, pp. 232–241. Springer, Heidelberg (2004)
Pelikan, M., Goldberg, D.E.: Escaping hierarchical traps with competent genetic algorithms. In: Spector, L., et al. (eds.) GECCO 2001, July 7-11, pp. 511–518. Morgan Kaufmann, San Francisco (2001)
Yu, T.L., Goldberg, D.E.: Conquering hierarchical difficulty by explicit chunking: substructural chromosome compression. In: GECCO 2006, pp. 1385–1392. ACM Press, NY (2006)
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220, 671–680 (1983)
van Dongen, S.: Graph Clustering by Flow Simulation. PhD thesis, U. of Utrecht (2000)
Brohée, S., van Helden, J.: Evaluation of clustering algorithms for protein-protein interaction networks. BMC Bioinformatics 7, 488 (2006)
Iclănzan, D., Dumitrescu, D.: Graph clustering based model building. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds.) PPSN XI. LNCS, vol. 6238, pp. 506–515. Springer, Heidelberg (2010)
Deb, K., Goldberg, D.E.: Analyzing deception in trap functions. In: Whitley, L.D. (ed.) Foundations of Genetic Algorithms 2, San Mateo, pp. 93–108. Morgan Kaufmann (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Iclănzan, D. (2014). Global Optimization of Multimodal Deceptive Functions. In: Blum, C., Ochoa, G. (eds) Evolutionary Computation in Combinatorial Optimisation. EvoCOP 2014. Lecture Notes in Computer Science, vol 8600. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44320-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-662-44320-0_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-44319-4
Online ISBN: 978-3-662-44320-0
eBook Packages: Computer ScienceComputer Science (R0)