Abstract
Black-box optimization (BBO) problems arise in numerous
scientic and engineering applications and are characterized by compu-
tationally intensive objective functions, which severely limit the number
of evaluations that can be performed. We present a robust set of features
that analyze the tness landscape of BBO problems and show how an
algorithm portfolio approach can exploit these general, problem indepen-
dent features and outperform the utilization of any single minimization
search strategy. We test our methodology on data from the GECCO
Workshop on BBO Benchmarking 2012, which contains 21 state-of-the-
art solvers run on 24 well-established functions.
scientic and engineering applications and are characterized by compu-
tationally intensive objective functions, which severely limit the number
of evaluations that can be performed. We present a robust set of features
that analyze the tness landscape of BBO problems and show how an
algorithm portfolio approach can exploit these general, problem indepen-
dent features and outperform the utilization of any single minimization
search strategy. We test our methodology on data from the GECCO
Workshop on BBO Benchmarking 2012, which contains 21 state-of-the-
art solvers run on 24 well-established functions.
| Originalsprog | Engelsk |
|---|---|
| Titel | Learning and Intelligent Optimization |
| Forlag | Springer |
| Publikationsdato | jan. 2013 |
| Sider | 30-36 |
| ISBN (Trykt) | 978-3-642-44972-7 |
| DOI | |
| Status | Udgivet - jan. 2013 |
| Begivenhed | Learning and Intelligent OptimizatioN Conference 2013 - Episcopate Museum Catania, Catania, Italien Varighed: 7 jan. 2013 → 11 jan. 2013 https://link.springer.com/book/10.1007/978-3-642-44973-4 |
Konference
| Konference | Learning and Intelligent OptimizatioN Conference 2013 |
|---|---|
| Lokation | Episcopate Museum Catania |
| Land/Område | Italien |
| By | Catania |
| Periode | 07/01/2013 → 11/01/2013 |
| Andet | Edited by Giuseppe Nicosia and Panos Pardalos |
| Internetadresse |
| Navn | Lecture Notes in Computer Science |
|---|---|
| ISSN | 0302-9743 |
Emneord
- Black-box optimization
- Fitness landscape analysis
- Algorithm portfolio
- Computationally intensive functions
- Optimization benchmarking