Recent Results on No-Free-Lunch Theorems for Optimization
| dc.creator | Igel, Christian | |
| dc.creator | Toussaint, Marc | |
| dc.date | 2003-03-31 | |
| dc.date.accessioned | 2026-07-07T03:19:33Z | |
| dc.date.available | 2026-07-07T03:19:33Z | |
| dc.description | 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 permutation (c.u.p.) and each target function in F is equally likely. In this paper, we first summarize some consequences of this theorem, which have been proven recently: The average number of evaluations needed to find a desirable (e.g., optimal) solution can be calculated; the number of subsets c.u.p. can be neglected compared to the overall number of possible subsets; and problem classes relevant in practice are not likely to be c.u.p. Second, as the main result, the NFL-theorem is extended. Necessary and sufficient conditions for NFL-results to hold are given for arbitrary, non-uniform distributions of target functions. This yields the most general NFL-theorem for optimization presented so far. | |
| dc.description | 10 pages, LaTeX, see http://www.neuroinformatik.rub.de/PROJECTS/SONN/ | |
| dc.identifier | https://arxiv.org/abs/cs/0303032 | |
| dc.identifier | http://arxiv.org/abs/cs/0303032 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/31507 | |
| dc.subject | Neural and Evolutionary Computing | |
| dc.subject | Optimization and Control | |
| dc.subject | Adaptation and Self-Organizing Systems | |
| dc.subject | G.1.6 | |
| dc.title | Recent Results on No-Free-Lunch Theorems for Optimization | |
| dc.type | text |