No free lunch theorems for optimization

  title={No free lunch theorems for optimization},
  author={David H. Wolpert and William G. Macready},
  journal={IEEE Trans. Evol. Comput.},
A framework is developed to explore the connection between effective optimization algorithms and the problems they are solving. A number of "no free lunch" (NFL) theorems are presented which establish that for any algorithm, any elevated performance over one class of problems is offset by performance over another class. These theorems result in a geometric interpretation of what it means for an algorithm to be well suited to an optimization problem. Applications of the NFL theorems to… 

No Free Lunch Theorem: A Review

The objective of this paper is to go through the main research efforts that contributed to this research field, reveal the main issues, and disclose those points that are helpful in understanding the hypotheses, the restrictions, or even the inability of applying No Free Lunch theorems.

Requirements for papers focusing on new or improved global optimization algorithms

The No-Free Lunch theorem (Wolpert and Macready 1997) provides an important limitation of global optimization algorithms that means that when a new or improved global optimization algorithm is proposed, it should be targeted towards a particular application or set of applications rather than tested against a fixed set of problems.

Conditions that Obviate the No-Free-Lunch Theorems for Optimization

This paper looks more closely at the NFL results and focuses on their implications for combinatorial problems typically faced by many researchers and practitioners, finding that only trivial subclasses of these problems fall under the NFL implications.

Recent Results on No-Free-Lunch Theorems for Optimization

The sharpened No-Free-Lunch-theorem (NFL-theorem) states that the performance of all optimization algorithms averaged over any finite set F of functions is equal if and only if F is closed under

A Graphical Model for Evolutionary Optimization

A statistical model of empirical optimization that admits the creation of algorithms with explicit and intuitively defined desiderata that provides a direct way to answer the traditionally difficult question of what algorithm is best matched to a particular class of functions.

A Study of Some Implications of the No Free Lunch Theorem

It is proved that each set of functions based on the distance to a given optimal solution, among which trap functions, onemax or the recently introduced onemix functions, and the NK-landscapes are not c.u.p. and thus the thesis of the sharpened No Free Lunch Theorem does not hold for them.

Searching for a Practical Evidence of the No Free Lunch Theorems

Several test functions for which Random Search performs better than all other considered algorithms have been evolved and show the effectiveness of the proposed evolutionary approach.

A framework for co-optimization algorithm performance and its application to worst-case optimization

Simple Explanation of the No Free Lunch Theorem of Optimization

  • Y. HoD. Pepyne
  • Computer Science
    Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228)
  • 2001
A framework is presented for conceptualizing optimization problems that leads to useful insights and a simple explanation of the No Free Lunch Theorem of Optimization.

No Free Lunch Theorems: Limitations and Perspectives of Metaheuristics

  • C. Igel
  • Computer Science
    Theory and Principled Methods for the Design of Metaheuristics
  • 2014
It is not likely that the preconditions of the NFL theorems are fulfilled for a problem class and thus differences between algorithms exist, therefore, tailored algorithms can exploit structure underlying the optimization problem.



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