Skip to main content

2018 | OriginalPaper | Buchkapitel

A Review of No Free Lunch Theorems, and Their Implications for Metaheuristic Optimisation

verfasst von : Thomas Joyce, J. Michael Herrmann

Erschienen in: Nature-Inspired Algorithms and Applied Optimization

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The No Free Lunch Theorem states that, averaged over all optimisation problems, all non-resampling optimisation algorithms perform equally well. In order to explain the relevance of these theorems for metaheuristic optimisation, we present a detailed discussion on the No Free Lunch Theorem, and various extensions including some which have not appeared in the literature so far. We then show that understanding the No Free Lunch theorems brings us to a position where we can ask about the specific dynamics of an optimisation algorithm, and how those dynamics relate to the properties of optimisation problems.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Wolpert, D.H., Macready, W.G.: No free lunch theorems for search. Technical report. SFI-TR-95-02-010, Santa Fe Institute (1995) Wolpert, D.H., Macready, W.G.: No free lunch theorems for search. Technical report. SFI-TR-95-02-010, Santa Fe Institute (1995)
2.
Zurück zum Zitat Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. In: IEEE Trans. Evol. Comput. 1.1, 67–82 (1997) Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. In: IEEE Trans. Evol. Comput. 1.1, 67–82 (1997)
3.
Zurück zum Zitat Whitley, D., Rowe, J.: Focused no free lunch theorems. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 811–818. ACM (2008) Whitley, D., Rowe, J.: Focused no free lunch theorems. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 811–818. ACM (2008)
4.
Zurück zum Zitat Whitley, D.: Functions as permutations: regarding no free lunch, walsh analysis and summary statistics. In: Parallel Problem Solving from Nature PPSN VI, pp. 169–178. Springer (2000) Whitley, D.: Functions as permutations: regarding no free lunch, walsh analysis and summary statistics. In: Parallel Problem Solving from Nature PPSN VI, pp. 169–178. Springer (2000)
5.
Zurück zum Zitat Culberson, J.C.: On the futility of blind search: an algorithmic view of ’no free lunch. Evol. Comput. 6(2), 109–127 (1998)CrossRef Culberson, J.C.: On the futility of blind search: an algorithmic view of ’no free lunch. Evol. Comput. 6(2), 109–127 (1998)CrossRef
6.
Zurück zum Zitat Lattimore, T., Hutter, M.: No free lunch versus Occam’s razor in supervised learning. In: Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence, pp. 223–235. Springer (2013) Lattimore, T., Hutter, M.: No free lunch versus Occam’s razor in supervised learning. In: Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence, pp. 223–235. Springer (2013)
7.
Zurück zum Zitat Serafino, L.: No Free Lunch Theorem and Bayesian probability theory: two sides of the same coin. Some implications for black-box optimization and metaheuristics. In: (2013). arXiv:1311.6041 Serafino, L.: No Free Lunch Theorem and Bayesian probability theory: two sides of the same coin. Some implications for black-box optimization and metaheuristics. In: (2013). arXiv:​1311.​6041
8.
Zurück zum Zitat English, T.: No more lunch: analysis of sequential search. In: Proceedings of the 2004 Congress on Evolutionary Computation CEC2004, Vol. 1, pp. 227–234. IEEE (2004) English, T.: No more lunch: analysis of sequential search. In: Proceedings of the 2004 Congress on Evolutionary Computation CEC2004, Vol. 1, pp. 227–234. IEEE (2004)
9.
Zurück zum Zitat English, T.: Optimization is easy and learning is hard in the typical function. In: Proceedings of the 2000 Congress on Evolutionary Computation, vol. 2, pp. 924–931. IEEE (2000) English, T.: Optimization is easy and learning is hard in the typical function. In: Proceedings of the 2000 Congress on Evolutionary Computation, vol. 2, pp. 924–931. IEEE (2000)
10.
Zurück zum Zitat English, T.: On the structure of sequential search: beyond ’no free lunch’. In: Evolutionary Computation in Combinatorial Optimization, pp. 95–103. Springer (2004) English, T.: On the structure of sequential search: beyond ’no free lunch’. In: Evolutionary Computation in Combinatorial Optimization, pp. 95–103. Springer (2004)
11.
Zurück zum Zitat Ho, Yu-Chi, Pepyne, D.L.: Simple explanation of the no-free-lunch theorem and its implications. J. Optim. Theory Appl. 115(3), 549–570 (2002)MathSciNetCrossRefMATH Ho, Yu-Chi, Pepyne, D.L.: Simple explanation of the no-free-lunch theorem and its implications. J. Optim. Theory Appl. 115(3), 549–570 (2002)MathSciNetCrossRefMATH
12.
Zurück zum Zitat Duéñez-Guzmán, E.A., Vose, M.D.: No free lunch and benchmarks. Evol. Comput. 21(2), 293–312 (2013)CrossRef Duéñez-Guzmán, E.A., Vose, M.D.: No free lunch and benchmarks. Evol. Comput. 21(2), 293–312 (2013)CrossRef
13.
Zurück zum Zitat Radcliffe, N.J., Surry, P.D.: Fundamental limitations on search algorithms: evolutionary computing in perspective. In: Computer Science Today, pp. 275–291. Springer (1995) Radcliffe, N.J., Surry, P.D.: Fundamental limitations on search algorithms: evolutionary computing in perspective. In: Computer Science Today, pp. 275–291. Springer (1995)
14.
Zurück zum Zitat Schumacher, C., Vose, M.D., Whitley, L.D.: The no free lunch and problem description length. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pp. 565–570 (2001) Schumacher, C., Vose, M.D., Whitley, L.D.: The no free lunch and problem description length. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pp. 565–570 (2001)
15.
Zurück zum Zitat Igel, C., Toussaint, M.: A no-free-lunch theorem for non-uniform distributions of target functions. J. Math. Model. Algorithm. 3(4), 313–322 (2005)MathSciNetCrossRefMATH Igel, C., Toussaint, M.: A no-free-lunch theorem for non-uniform distributions of target functions. J. Math. Model. Algorithm. 3(4), 313–322 (2005)MathSciNetCrossRefMATH
16.
Zurück zum Zitat Droste, S., Thomas, J., Ingo, W.: Optimization with randomized search heuristics—the (A) NFL theorem, realistic scenarios, and difficult functions. Theor. Comput. Sci. 287(1), 131–144 (2002)MathSciNetCrossRefMATH Droste, S., Thomas, J., Ingo, W.: Optimization with randomized search heuristics—the (A) NFL theorem, realistic scenarios, and difficult functions. Theor. Comput. Sci. 287(1), 131–144 (2002)MathSciNetCrossRefMATH
17.
Zurück zum Zitat Griffiths, E.J., Orponen, P.: Optimization, block designs and no free lunch theorems. Inf. Process. Lett. 94(2), 55–61 (2005)MathSciNetCrossRefMATH Griffiths, E.J., Orponen, P.: Optimization, block designs and no free lunch theorems. Inf. Process. Lett. 94(2), 55–61 (2005)MathSciNetCrossRefMATH
18.
Zurück zum Zitat Corne, D.W., Knowles, J.D.: No free lunch and free leftovers theorems for multiobjective optimisation problems. In: Evolutionary Multi- Criterion Optimization. Springer (2003) Corne, D.W., Knowles, J.D.: No free lunch and free leftovers theorems for multiobjective optimisation problems. In: Evolutionary Multi- Criterion Optimization. Springer (2003)
19.
Zurück zum Zitat Service, T.C.: A no free Lunch theorem for multi-objective optimization. Inf. Process. Lett. 110(21), 917–923 (2010)MathSciNetCrossRef Service, T.C.: A no free Lunch theorem for multi-objective optimization. Inf. Process. Lett. 110(21), 917–923 (2010)MathSciNetCrossRef
20.
Zurück zum Zitat Corne, D., Knowles, J.: Some multiobjective optimizers are better than others. In: The 2003 Congress on Evolutionary Computation, Vol. 4, pp. 2506–2512. IEEE (2003) Corne, D., Knowles, J.: Some multiobjective optimizers are better than others. In: The 2003 Congress on Evolutionary Computation, Vol. 4, pp. 2506–2512. IEEE (2003)
21.
Zurück zum Zitat Everitt, T.: Universal indution and optimisation: no free lunch? In: Thesis (2013) Everitt, T.: Universal indution and optimisation: no free lunch? In: Thesis (2013)
22.
Zurück zum Zitat Auger, A., Teytaud, O.: Continuous lunches are free plus the design of optimal optimization algorithms. Algorithmica 57(1), 121–146 (2010)MathSciNetCrossRefMATH Auger, A., Teytaud, O.: Continuous lunches are free plus the design of optimal optimization algorithms. Algorithmica 57(1), 121–146 (2010)MathSciNetCrossRefMATH
23.
Zurück zum Zitat Rowe, J., Vose, M., Wright, A.: Reinterpreting no free lunch. Evol. Comput. 17(1), 117–129 (2009)CrossRef Rowe, J., Vose, M., Wright, A.: Reinterpreting no free lunch. Evol. Comput. 17(1), 117–129 (2009)CrossRef
25.
Zurück zum Zitat Yang, X.-S.: Free lunch or no free lunch: that is not just a question? Int. J. Artif. Intell. Tools 21(03), 1240010 (2012)CrossRef Yang, X.-S.: Free lunch or no free lunch: that is not just a question? Int. J. Artif. Intell. Tools 21(03), 1240010 (2012)CrossRef
26.
Zurück zum Zitat Yang, X.-S., Deb, S.: Cuckoo search via Lévy flights. In: World Congress on Nature & Biologically Inspired Computing, pp. 210–214. IEEE (2009) Yang, X.-S., Deb, S.: Cuckoo search via Lévy flights. In: World Congress on Nature & Biologically Inspired Computing, pp. 210–214. IEEE (2009)
27.
Zurück zum Zitat Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766. Springer (2011) Kennedy, J.: Particle swarm optimization. In: Encyclopedia of Machine Learning, pp. 760–766. Springer (2011)
28.
Zurück zum Zitat Poli, R.: Mean and variance of the sampling distribution of particle swarm optimizers during stagnation. IEEE Trans. Evol. Comput. 13(4), 712–721 (2009)CrossRef Poli, R.: Mean and variance of the sampling distribution of particle swarm optimizers during stagnation. IEEE Trans. Evol. Comput. 13(4), 712–721 (2009)CrossRef
29.
Zurück zum Zitat Erskine, A., Joyce, T., Herrmann, J.M.: Parameter selection in particle Swarm optimisation from stochastic stability analysis. In: International Conference on Swarm Intelligence, pp. 161–172. Springer (2016) Erskine, A., Joyce, T., Herrmann, J.M.: Parameter selection in particle Swarm optimisation from stochastic stability analysis. In: International Conference on Swarm Intelligence, pp. 161–172. Springer (2016)
30.
Zurück zum Zitat Serafino, L.: Optimizing without derivatives: what does the no free lunch theorem actually say? In: Notices of the AMS 61.7 (2014) Serafino, L.: Optimizing without derivatives: what does the no free lunch theorem actually say? In: Notices of the AMS 61.7 (2014)
31.
Zurück zum Zitat Ben-David, S., Srebro, N., Urner, R.: Universal learning versus no free lunch results. In: Philosophy and Machine Learning Workshop NIPS (2011) Ben-David, S., Srebro, N., Urner, R.: Universal learning versus no free lunch results. In: Philosophy and Machine Learning Workshop NIPS (2011)
32.
Zurück zum Zitat David, J.C.M.: Information theory, inference and learning algorithms. Cambridge University Press (2003) David, J.C.M.: Information theory, inference and learning algorithms. Cambridge University Press (2003)
33.
Zurück zum Zitat Streeter, M.J.: Two broad classes of functions for which a no free lunch result does not hold. In: Genetic and Evolutionary Computation-GECCO, pp. 1418–1430 Springer (2003) Streeter, M.J.: Two broad classes of functions for which a no free lunch result does not hold. In: Genetic and Evolutionary Computation-GECCO, pp. 1418–1430 Springer (2003)
Metadaten
Titel
A Review of No Free Lunch Theorems, and Their Implications for Metaheuristic Optimisation
verfasst von
Thomas Joyce
J. Michael Herrmann
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-67669-2_2