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

By Y. Ho, D. Pepyne. 2002

543 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

Evaluating defect prediction approaches: a benchmark and an extensive comparison (2011, background): “The “No Free Lunch” theorem (Ho and Pepyne 2002) states that if ones does not have specific knowledge about a particular problem domain, then every approach to optimization problems will have the same average performance on the set of all inputs—even random approaches.”
No silver bullets in correlative ecological niche modelling: insights from testing among many potential algorithms for niche estimation (2015, background): “This outcome was expected on conceptual grounds (Ho & Pepyne 2002) and was corroborated in the results of this empirical test.”
How the brain might work: a hierarchical and temporal model for learning and recognition (2008, background): “The No Free Lunch (NFL) theorems [110, 50] for learning teach us that no learning algorithm has an inherent superiority over another learning algorithm for all learning problems.”
Chemical-Reaction-Inspired Metaheuristic for Optimization (2010, background): “Ho and Pepyne [25] further elaborate upon the idea, showing that it is theoretically impossible to have a best general-purpose universal optimization strategy, and the only way for one strategy to be superior to the others is when we focus on a particular class of problems only.”
From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors (2022, background): “This confirms that concept drift detection adheres to the No Free Lunch Theorem [180], and a universal approach that copes with all machine learning problems are challenging to find [38].”
A social spider algorithm for global optimization (2015, background): “Superior performing algorithms are available if particular classes of problems are considered [47], or general but real-world ones [48].”
Automated assessment of psychiatric disorders using speech: A systematic review (2019, methodology): “sample size, preprocessing, feature selection, and model, which will all depend on the specific data being used (ie, different algorithms on different datasets will make different speed-accuracy-complexity tradeoffs and therefore there is no universally best model; see “no free lunch theorem”).(71)”
Climate change may drive cave spiders to extinction (2018, background): “Considering the conceptual ground of the "no free lunch" theorem (Ho and Pepyne 2002), Qiao et al. (2015) recently suggested that no single best A cc ep te d A rt ic le ‘This article is protected by copyright.”
Optimizing connection weights in neural networks using the whale optimization algorithm (2016, methodology): “According to the no-free-lunch theorem (NFL), there is no superior optimization algorithm in all optimization problems (Wolpert and Macready 1997; Ho and Pepyne 2002).”
Biogeography-Based Optimization (2008, background): “Such a general statement would be an oversimplification, especially in view of the no free lunch theorem [32].”
An efficient modified grey wolf optimizer with Lévy flight for optimization tasks (2017, background): “Based on the theorem of ''no free lunch'' (NFL), a universally best optimizer for all classes of problems does not exist, [72, 73].”
Let a biogeography-based optimizer train your Multi-Layer Perceptron (2014, methodology): “It has been proven by the well-known No Free Lunch theorem (NFL) that there is no heuristic algorithm best suited to solving all optimization problems [22,77,7,39].”
Equivalent circuit for the brushless doubly fed machine (BDFM) including parameter estimation and experimental verification (2005, methodology): “Furthermore because it requires no gradient information there is no need for any a priori information about the particular cost function, and it can be shown that without any such information then a simple random search is as efficient as any other method, in reaching the global minimum [24].”
A novel population initialization method for accelerating evolutionary algorithms (2007, background): “This theorem states that “A general-purpose universal optimization strategy is theoretically impossible, and the only way one strategy can outperform another is if it is specialized to the specific problem under consideration [26].”
Swarm Intelligence Algorithms for Feature Selection: A Review (2018, background): “Firstly, the No Free Lunch Theorem [110] tells us a general-purpose, universal optimization strategy is impossible [111].”