Abstract
This paper describes a class of random additively decomposable problems (rADPs) with and without interactions between the subproblems. The paper then tests the hierarchical Bayesian optimization algorithm (hBOA) and other evolutionary algorithms on a large number of random instances of the proposed class of problems. The results show that hBOA can scalably solve rADPs and that it significantly outperforms all other methods included in the comparison. Furthermore, the results provide a number of interesting insights into both the difficulty of a broad class of decomposable problems as well as the sensitivity of various evolutionary algorithms to different sources of problem difficulty. rADPs can be used to test other optimization algorithms.
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
Ackley, D.H.: An empirical study of bit vector function optimization. Genetic Algorithms and Simulated Annealing, 170–204 (1987)
Deb, K., Goldberg, D.E.: Analyzing deception in trap functions. IlliGAL Report No. 91009, University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, IL (1991)
Cheeseman, P., Kanefsky, B., Taylor, W.M.: Where the really hard problems are. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 1991), pp. 331–337 (1991)
Santarelli, S., Goldberg, D.E., Yu, T.L.: Optimization of a constrained feed network for an antenna array using simple and competent genetic algorithm techniques. In: Proceedings of the Workshop Military and Security Application of Evolutionary Computation (MSAEC 2004) (2004)
Barahona, F., Maynard, R., Rammal, R., Uhry, J.: Morphology of ground states of a two dimensional frustration model. J. Phys. A 15, 673 (1982)
Papadimitriou, C.H.: The Euclidean travelling salesman problem is NP-complete. Theoretical Computer Science 4, 237–244 (1977)
Gao, Y., Culberson, J.: Space complexity of EDA. Evolutionary Computation 13(1), 125–143 (2005)
Gao, Y., Culberson, J.: On the treewidth of NK landscapes. In: Genetic and Evolutionary Computation Conference (GECCO 2003), vol. II, pp. 948–954 (2003)
Weinberger, E.D.: Local properties of kauffman’s N-k model: A tunably rugged enegy landscape. Physical Review A 44(10), 6399–6413 (1991)
Mühlenbein, H., Paaß, G.: From recombination of genes to the estimation of distributions I. Binary parameters. Parallel Problem Solving from Nature, 178–187 (1996)
Pelikan, M., Goldberg, D.E.: Escaping hierarchical traps with competent genetic algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001), pp. 511–518 (2001); Also IlliGAL Report No. 2000020
Baluja, S.: Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning. Tech. Rep. No. CMU-CS-94-163, Carnegie Mellon University, Pittsburgh, PA (1994)
Pelikan, M., Goldberg, D.E., Lobo, F.: A survey of optimization by building and using probabilistic models. Computational Optimization and Applications 21(1), 5–20 (2002); Also IlliGAL Report No. 99018
Larrañaga, P., Lozano, J.A. (eds.): Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer, Boston (2002)
Pelikan, M.: Hierarchical Bayesian optimization algorithm: Toward a new generation of evolutionary algorithms. Springer, Heidelberg (2005)
Thierens, D., Goldberg, D.E., Pereira, A.G.: Domino convergence, drift, and the temporal-salience structure of problems. In: Proceedings of the International Conference on Evolutionary Computation (ICEC 1998), pp. 535–540 (1998)
Mühlenbein, H.: How genetic algorithms really work: I. Mutation and Hillclimbing. In: Männer, R., Manderick, B. (eds.) Parallel Problem Solving from Nature, pp. 15–25. Elsevier Science, Amsterdam, Netherlands (1992)
Thierens, D.: Analysis and design of genetic algorithms. PhD thesis, Katholieke Universiteit Leuven, Leuven, Belgium (1995)
Goldberg, D.E.: The design of innovation: Lessons from and for competent genetic algorithms. Genetic Algorithms and Evolutionary Computation, vol. 7. Kluwer Academic Publishers, Dordrecht (2002)
Holland, J.H.: Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor (1975)
Goldberg, D.E.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading (1989)
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
Pelikan, M., Sastry, K., Butz, M.V., Goldberg, D.E. (2006). Performance of Evolutionary Algorithms on Random Decomposable Problems. In: Runarsson, T.P., Beyer, HG., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds) Parallel Problem Solving from Nature - PPSN IX. PPSN 2006. Lecture Notes in Computer Science, vol 4193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11844297_80
Download citation
DOI: https://doi.org/10.1007/11844297_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38990-3
Online ISBN: 978-3-540-38991-0
eBook Packages: Computer ScienceComputer Science (R0)