No Free Lunch Theorems: Limitations and Perspectives of Metaheuristics
By Christian Igel. 2014
33 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Swagatam Das
- Ponnuthurai Nagaratnam Suganthan
- Seyedali Mirjalili
- Xin-She Yang
- Seyed Mohammad Mirjalili
- Francisco Herrera
- Pinar Çivicioglu
- Daniel Molina
In Computational Theory and Mathematics
- Swagatam Das
- Ponnuthurai Nagaratnam Suganthan
- Xin-She Yang
- Seyedali Mirjalili
- Seyed Mohammad Mirjalili
- Francisco Herrera
- Pinar Çivicioglu
- Carlos Artemio Coello Coello
Citations in the corpus
- Hyper-Parameter Optimization: A Review of Algorithms and Applications (2020, background): “The no free lunch theorem suggests that the computational cost for any optimization problem is the same for all problems and no solution offers a shortcut (Wolpert and Macready, 1997; Igel, 2014).”
- Bio-inspired computation: Where we stand and what's next (2019, background): “Indeed, the lack of a universally outperforming meta-heuristic for all kind of optimization problems yields from the well-known No Free Lunch Theorem for optimization, originally introduced in [47], further elaborated in [48, 49, 50, 51], and recently revisited in [52].”