IDEAS home Printed from https://ideas.repec.org/a/spr/joptap/v115y2002i3d10.1023_a1021251113462.html
   My bibliography  Save this article

Simple Explanation of the No-Free-Lunch Theorem and Its Implications

Author

Listed:
  • Y.C. Ho

    (Harvard University)

  • D.L. Pepyne

    (Harvard University)

Abstract

The no-free-lunch theorem of optimization (NFLT) is an impossibility theorem telling us that a general-purpose, universal optimization strategy is impossible. The only way one strategy can outperform another is if it is specialized to the structure of the specific problem under consideration. Since optimization is a central human activity, an appreciation of the NFLT and its consequences is essential. In this paper, we present a framework for conceptualizing optimization that leads to a simple but rigorous explanation of the NFLT and its implications.

Suggested Citation

  • Y.C. Ho & D.L. Pepyne, 2002. "Simple Explanation of the No-Free-Lunch Theorem and Its Implications," Journal of Optimization Theory and Applications, Springer, vol. 115(3), pages 549-570, December.
  • Handle: RePEc:spr:joptap:v:115:y:2002:i:3:d:10.1023_a:1021251113462
    DOI: 10.1023/A:1021251113462
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1023/A:1021251113462
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1023/A:1021251113462?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. David H. Wolpert & William G. Macready, 1995. "No Free Lunch Theorems for Search," Working Papers 95-02-010, Santa Fe Institute.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kutlu Onay, Funda, 2023. "A novel improved chef-based optimization algorithm with Gaussian random walk-based diffusion process for global optimization and engineering problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 212(C), pages 195-223.
    2. Hernán Peraza-Vázquez & Adrián Peña-Delgado & Prakash Ranjan & Chetan Barde & Arvind Choubey & Ana Beatriz Morales-Cepeda, 2021. "A Bio-Inspired Method for Mathematical Optimization Inspired by Arachnida Salticidade," Mathematics, MDPI, vol. 10(1), pages 1-32, December.
    3. Zekharya Danin & Abhishek Sharma & Moshe Averbukh & Arabinda Meher, 2022. "Improved Moth Flame Optimization Approach for Parameter Estimation of Induction Motor," Energies, MDPI, vol. 15(23), pages 1-13, November.
    4. Hegazy Rezk & Abdul Ghani Olabi & Rania M. Ghoniem & Mohammad Ali Abdelkareem, 2023. "Optimized Fractional Maximum Power Point Tracking Using Bald Eagle Search for Thermoelectric Generation System," Energies, MDPI, vol. 16(10), pages 1-15, May.
    5. Felipe Cisternas-Caneo & Broderick Crawford & Ricardo Soto & Giovanni Giachetti & Álex Paz & Alvaro Peña Fritz, 2024. "Chaotic Binarization Schemes for Solving Combinatorial Optimization Problems Using Continuous Metaheuristics," Mathematics, MDPI, vol. 12(2), pages 1-39, January.
    6. Townsend Peterson, A., 2007. "Why not WhyWhere: The need for more complex models of simpler environmental spaces," Ecological Modelling, Elsevier, vol. 203(3), pages 527-530.
    7. Marcelo Becerra-Rozas & José Lemus-Romani & Felipe Cisternas-Caneo & Broderick Crawford & Ricardo Soto & José García, 2022. "Swarm-Inspired Computing to Solve Binary Optimization Problems: A Backward Q-Learning Binarization Scheme Selector," Mathematics, MDPI, vol. 10(24), pages 1-30, December.
    8. Deb, Sanchari & Gao, Xiao-Zhi & Tammi, Kari & Kalita, Karuna & Mahanta, Pinakeswar, 2021. "A novel chicken swarm and teaching learning based algorithm for electric vehicle charging station placement problem," Energy, Elsevier, vol. 220(C).
    9. Alexander Gerling & Holger Ziekow & Andreas Hess & Ulf Schreier & Christian Seiffer & Djaffar Ould Abdeslam, 2022. "Comparison of algorithms for error prediction in manufacturing with automl and a cost-based metric," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 555-573, February.
    10. Fabio Caraffini & Giovanni Iacca, 2020. "The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms," Mathematics, MDPI, vol. 8(5), pages 1-31, May.
    11. Modiri-Delshad, Mostafa & Aghay Kaboli, S. Hr. & Taslimi-Renani, Ehsan & Rahim, Nasrudin Abd, 2016. "Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options," Energy, Elsevier, vol. 116(P1), pages 637-649.
    12. Thomas Wong & Mauricio Barahona, 2023. "Deep incremental learning models for financial temporal tabular datasets with distribution shifts," Papers 2303.07925, arXiv.org, revised Oct 2023.
    13. Marcelo Becerra-Rozas & José Lemus-Romani & Felipe Cisternas-Caneo & Broderick Crawford & Ricardo Soto & Gino Astorga & Carlos Castro & José García, 2022. "Continuous Metaheuristics for Binary Optimization Problems: An Updated Systematic Literature Review," Mathematics, MDPI, vol. 11(1), pages 1-32, December.
    14. Alaa A. K. Ismaeel & Essam H. Houssein & Doaa Sami Khafaga & Eman Abdullah Aldakheel & Ahmed S. AbdElrazek & Mokhtar Said, 2023. "Performance of Osprey Optimization Algorithm for Solving Economic Load Dispatch Problem," Mathematics, MDPI, vol. 11(19), pages 1-19, September.
    15. Kottath, Rahul & Singh, Priyanka, 2023. "Influencer buddy optimization: Algorithm and its application to electricity load and price forecasting problem," Energy, Elsevier, vol. 263(PC).
    16. Mokhtar Said & Ali M. El-Rifaie & Mohamed A. Tolba & Essam H. Houssein & Sanchari Deb, 2021. "An Efficient Chameleon Swarm Algorithm for Economic Load Dispatch Problem," Mathematics, MDPI, vol. 9(21), pages 1-14, November.
    17. Si, Binghui & Tian, Zhichao & Jin, Xing & Zhou, Xin & Shi, Xing, 2019. "Ineffectiveness of optimization algorithms in building energy optimization and possible causes," Renewable Energy, Elsevier, vol. 134(C), pages 1295-1306.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jui-Sheng Chou & Dinh-Nhat Truong & Chih-Fong Tsai, 2021. "Solving Regression Problems with Intelligent Machine Learner for Engineering Informatics," Mathematics, MDPI, vol. 9(6), pages 1-25, March.
    2. Sevvandi Kandanaarachchi & Mario A Munoz & Rob J Hyndman & Kate Smith-Miles, 2018. "On normalization and algorithm selection for unsupervised outlier detection," Monash Econometrics and Business Statistics Working Papers 16/18, Monash University, Department of Econometrics and Business Statistics.
    3. Kamran Zolfi, 2023. "Gold rush optimizer: A new population-based metaheuristic algorithm," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 33(1), pages 113-150.
    4. Murtadha Al-Kaabi & Virgil Dumbrava & Mircea Eremia, 2022. "A Slime Mould Algorithm Programming for Solving Single and Multi-Objective Optimal Power Flow Problems with Pareto Front Approach: A Case Study of the Iraqi Super Grid High Voltage," Energies, MDPI, vol. 15(20), pages 1-33, October.
    5. Abdel-Rahman Hedar & Emad Mabrouk & Masao Fukushima, 2011. "Tabu Programming: A New Problem Solver Through Adaptive Memory Programming Over Tree Data Structures," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 373-406.
    6. Muangkote, Nipotepat & Sunat, Khamron & Chiewchanwattana, Sirapat & Kaiwinit, Sirilak, 2019. "An advanced onlooker-ranking-based adaptive differential evolution to extract the parameters of solar cell models," Renewable Energy, Elsevier, vol. 134(C), pages 1129-1147.
    7. Sharifian, Yeganeh & Abdi, Hamdi, 2023. "Solving multi-area economic dispatch problem using hybrid exchange market algorithm with grasshopper optimization algorithm," Energy, Elsevier, vol. 267(C).
    8. Díaz–Pachón, Daniel Andrés & Sáenz, Juan Pablo & Rao, J. Sunil, 2020. "Hypothesis testing with active information," Statistics & Probability Letters, Elsevier, vol. 161(C).
    9. Yi Peng & Gang Kou & Guoxun Wang & Honggang Wang & Franz I. S. Ko, 2009. "Empirical Evaluation Of Classifiers For Software Risk Management," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 8(04), pages 749-767.
    10. Zhang, Xueying & Li, Ruixian & Zhang, Bo & Yang, Yunxiang & Guo, Jing & Ji, Xiang, 2019. "An instance-based learning recommendation algorithm of imbalance handling methods," Applied Mathematics and Computation, Elsevier, vol. 351(C), pages 204-218.
    11. Peter F. Stadler & Gunjter P. Wagner, 1996. "The Algebraic Theory of Recombination Spaces," Working Papers 96-07-046, Santa Fe Institute.
    12. L. Ingber, 1996. "Adaptive simulated annealing (ASA): Lessons learned," Lester Ingber Papers 96as, Lester Ingber.
    13. Christopher Ifeanyi Eke & Azah Anir Norman & Liyana Shuib, 2021. "Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-32, June.
    14. William G. Macready & David H. Wolpert, 1995. "What Makes an Optimization Problem Hard?," Working Papers 95-05-046, Santa Fe Institute.
    15. Galioto, Francesco & Battilani, Adriano, 2021. "Agro-economic simulation for day by day irrigation scheduling optimisation," Agricultural Water Management, Elsevier, vol. 248(C).
    16. Agarwal, Anurag & Colak, Selcuk & Eryarsoy, Enes, 2006. "Improvement heuristic for the flow-shop scheduling problem: An adaptive-learning approach," European Journal of Operational Research, Elsevier, vol. 169(3), pages 801-815, March.
    17. Murtadha Al-Kaabi & Virgil Dumbrava & Mircea Eremia, 2022. "Single and Multi-Objective Optimal Power Flow Based on Hunger Games Search with Pareto Concept Optimization," Energies, MDPI, vol. 15(22), pages 1-31, November.
    18. William G. Macready & David H. Wolpert, 1996. "On 2-Armed Gaussian Bandits and Optimization," Working Papers 96-03-009, Santa Fe Institute.
    19. Wang, Sinan & Zhao, Fuquan & Liu, Zongwei & Hao, Han, 2017. "Heuristic method for automakers' technological strategy making towards fuel economy regulations based on genetic algorithm: A China's case under corporate average fuel consumption regulation," Applied Energy, Elsevier, vol. 204(C), pages 544-559.
    20. Kimbrough, Steven Orla & Koehler, Gary J. & Lu, Ming & Wood, David Harlan, 2008. "On a Feasible-Infeasible Two-Population (FI-2Pop) genetic algorithm for constrained optimization: Distance tracing and no free lunch," European Journal of Operational Research, Elsevier, vol. 190(2), pages 310-327, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joptap:v:115:y:2002:i:3:d:10.1023_a:1021251113462. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.