Hostname: page-component-7dd5485656-frp75 Total loading time: 0 Render date: 2025-10-28T13:42:55.065Z Has data issue: false hasContentIssue false
  • English
  • Français

Towards a theory of practice in metaheuristics design: A machine learning perspective

Published online by Cambridge University Press:  20 July 2006

Mauro Birattari ,
Mark Zlochin  and
Marco Dorigo
Mauro Birattari
Affiliation:
IRIDIA, Université Libre de Bruxelles, Brussels, Belgium; mbiro@ulb.ac.be; mzlochin@ulb.ac.be; mdorigo@ulb.ac.be
Mark Zlochin
Affiliation:
IRIDIA, Université Libre de Bruxelles, Brussels, Belgium; mbiro@ulb.ac.be; mzlochin@ulb.ac.be; mdorigo@ulb.ac.be
Marco Dorigo
Affiliation:
IRIDIA, Université Libre de Bruxelles, Brussels, Belgium; mbiro@ulb.ac.be; mzlochin@ulb.ac.be; mdorigo@ulb.ac.be
Get access

Abstract

A number of methodological papers published during the last yearstestify that a need for a thorough revision of the researchmethodology is felt by the operations research community – see, forexample, [Barr et al., J. Heuristics1 (1995) 9–32; Eiben and Jelasity,Proceedings of the 2002 Congress on Evolutionary Computation (CEC'2002) 582–587; Hooker,J. Heuristics1 (1995) 33–42; Rardin and Uzsoy,J. Heuristics7 (2001) 261–304]. In particular, theperformance evaluation of nondeterministic methods, including widelystudied metaheuristics such as evolutionary computation and ant colonyoptimization, requires the definition of new experimental protocols.A careful and thorough analysis of the problem of evaluatingmetaheuristics reveals strong similarities between this problem andthe problem of evaluating learning methods in the machine learningfield.In this paper, we show that several conceptual tools commonly used inmachine learning – such as, for example, the probabilistic notion ofclass of instances and the separation between the training and thetesting datasets – fit naturally in the context of metaheuristicsevaluation.Accordingly, we propose and discuss some principles inspired by theexperimental practice in machine learning for guiding the performanceevaluation of optimization algorithms.Among these principles, a clear separation between the instances thatare used for tuning algorithms and those that are used in the actualevaluation is particularly important for a proper assessment.

Keywords

Information

Type
Research Article
Information
RAIRO - Theoretical Informatics and Applications , Volume 40 , Issue 2: Alberto Bertoni: Climbing summits , April 2006 , pp. 353 - 369
Copyright
© EDP Sciences, 2006

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Barr, R.S, Golden, B.L., Kelly, J.P., Resende, M.G.C. and Stewart, W.R., Designing and reporting computational experiments with heuristic methods. J. Heuristics 1 (1995) 9–32. CrossRef
M. Birattari, The Problem of Tuning Metaheuristics, as Seen from a Machine Learning Perspective. Ph.D. thesis, Université Libre de Bruxelles, Brussels, Belgium (2004).
M. Birattari, T. Stützle, L. Paquete and K. Varrentrapp, A racing algorithm for configuring metaheuristics, in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), edited by W.B. Langdon, et al. Morgan Kaufmann Publishers, San Francisco, CA (2002) 11–18.
Demenage, M., Grisoni, P. and Paschos, V.Th., Differential approximation algorithms for some combinatorial optimization problems. Theoret. Comput. Sci. 209 (1998) 107–122. CrossRef
Dorigo, M. and Blum, C., Ant colony optimization theory: A survey. Theoret. Comput. Sci. 344 (2005) 243–278. CrossRef
M. Dorigo and G. Di Caro, The Ant Colony Optimization meta-heuristic, in New Ideas in Optimization, edited by D. Corne, M. Dorigo and F. Glover. McGraw Hill, London, UK (1999) 11–32.
Dorigo, M., Di Caro, G. and Gambardella, L. M., Ant algorithms for discrete optimization. Artificial Life 5 (1999) 137–172. CrossRef
M. Dorigo, V. Maniezzo and A. Colorni, Ant System: Optimization by a colony of cooperating agents. IEEE Trans. Systems, Man, and Cybernetics – Part B 26 (1996) 29–41.
M. Dorigo and T. Stützle, Ant Colony Optimization. MIT Press, Cambridge, MA (2004).
A.E. Eiben and M. Jelasity, A Critical Note on Experimental Research Methodology in EC, in Proceedings of the 2002 Congress on Evolutionary Computation (CEC'2002), Piscataway, NJ, IEEE Press (2002) 582–587.
L.J. Fogel, A.J. Owens and M.J. Walsh, Artificial Intelligence Through Simulated Evolution. John Wiley & Sons, New York, NY (1966).
M.R. Garey and D.S. Johnson, Computers and Intractability: A Guide to the Theory of ${\cal NP}$ -Completeness. Freeman, San Francisco, CA (1979).
Glover, F., Tabu search – part I. ORSA J. Comput. 1 (1989) 190–206. CrossRef
Glover, F., Tabu search – part II. ORSA J. Comput. 2 (1990) 4–32. CrossRef
D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA (1989).
Hassin, R. and Khuller, S., Z-approximations. J. Algorithms 41 (2001) 429–442. CrossRef
J. Holland, Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI (1975).
Hooker, J.N., Testing heuristics: we have it all wrong. J. Heuristics 1 (1995) 33–42. CrossRef
D.S. Johnson, A theoretician's guide to the experimental analysis of algorithms, in Data structures, near neighbor searches, and methodology: 5th and 6th DIMACS implementation challenges. American Mathematical Society, Providence, RI (2002) 215–250.
S.A. Kauffman, The Origins of Order. Self-Organization and Selection in Evolution. Oxford University Press, Oxford, UK (1993).
Kirkpatrick, S., Gelatt Jr, C.D.. and M.P. Vecchi, Optimization by simulated annealing. Science 220 (1983) 671–680. CrossRef
Liang, K., Yao, X. and Newton, C., Adapting self-adaptive parameters in evolutionary algorithms. Appl. Intell. 15 (2001) 171–180. CrossRef
H.R. Lourenço, O. Martin and T. Stützle, Iterated local search, in Handbook of Metaheuristics. International Series in Operations Research & Management Science, edited by F. Glover and G. Kochenberger. Kluwer Academic Publishers, Norwell, MA 57 (2002) 321–353.
D.G. Luenberger, Introduction to Linear and Nonlinear Programming. Addison-Wesley Publishing Company, Reading, MA (1973).
O. Maron and A.W. Moore, Hoeffding races: Accelerating model selection search for classification and function approximation, in Advances in Neural Information Processing Systems, edited by J.D. Cowan, G. Tesauro and J. Alspector. Morgan Kaufmann Publishers, San Francisco, CA 6 (1994) 59–66.
McGeogh, C.C., Toward an experimental method for algorithm simulation. INFORMS J. Comput. 2 (1996) 1–15. CrossRef
Z. Michalewicz and D.B. Fogel, How to Solve it: Modern Heuristics. Springer-Verlag, Berlin, Germany (2000).
A.W. Moore and M.S. Lee, Efficient algorithms for minimizing cross validation error, in International Conference on Machine Learning. Morgan Kaufmann Publishers, San Francisco, CA (1994) 190–198.
Nelson, B.L., Swann, J., Goldsman, D. and Song, W., Simple procedures for selecting the best simulated system when the number of alternatives is large. Oper. Res. 49 (2001) 950–963. CrossRef
Rardin, R.R. and Uzsoy, R., Experimental evaluation of heuristic optimization algorithms: A tutorial. J. Heuristics 7 (2001) 261–304. CrossRef
I. Rechenberg, Evolutionsstrategie – Optimierung technischer Systeme nach Prinzipien der biologischen Information. Fromman Verlag, Freiburg, Germany (1973).
H.-P. Schwefel, Numerical Optimization of Computer Models. John Wiley & Sons, Chichester, UK (1981).
I. Sommerville, Software Engineering. Addison Wesley, Harlow, UK, sixth edition (2001).
M. Toussaint, Self-adaptive exploration in evolutionary search. Technical Report IRINI-2001-05, Institut für Neuroinformatik, Ruhr-Universität Bochum, Bochum, Germany (2001).
Wolpert, D.H. and Macready, W.G., No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1 (1997) 67–82. CrossRef
Zemel, E., Measuring the quality of approximate solutions to zero-one programming problems. Math. Oper. Res. 6 (1981) 319–332. CrossRef
Zlochin, M., Birattari, M., Meuleau, N. and Dorigo, M., Model-based search for combinatorial optimization: A critical survey. Ann. Oper. Res. 131 (2004) 375–395. CrossRef
M. Zlochin and M. Dorigo, Model based search for combinatorial optimization: A comparative study, in Parallel Problem Solving from Nature – PPSN VII, edited by M. Guervós, J.J. et al. Springer Verlag, Berlin, Germany (2002) 651–661.