Name
Title | ||
|---|---|---|
Adaptive Function Value Warping for Surrogate Model Assisted Evolutionary Optimization |
Abstract | ||
|---|---|---|
Surrogate modelling techniques have the potential to reduce the number of objective function evaluations needed to solve black-box optimization problems. Most surrogate modelling techniques in use with evolutionary algorithms today do not preserve the desirable invariance to order-preserving transformations of objective function values of the underlying algorithms. We propose adaptive function value warping as a tool aiming to reduce the sensitivity of algorithm behaviour to such transformations. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/978-3-031-14714-2_6 | Parallel Problem Solving from Nature – PPSN XVII |
DocType | ISSN | Citations |
Conference | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abbasnejad Amir | 1 | 0 | 0.34 |
| dirk v arnold | 2 | 503 | 49.25 |