Name
Title | ||
|---|---|---|
Tuning optimization algorithms for real-world problems by means of surrogate modeling |
Abstract | ||
|---|---|---|
The case-specific tuning of parameters of optimization metaheuristics like evolutionary algorithms almost always leads to significant improvements in performance. But if the evaluation of the objective function is computationally expensive, which is typically the case for real-worlds problems, an extensive parameter tuning phase on the original problem is prohibitive. Therefore we have developed another approach: Provided that a (computationally cheap) surrogate model is available that reflects the structural characteristics of the original problem then the parameter tuning can be run on the surrogate problem before using the best parameters thereby identified for the metaheuristic when optimizing the original problem. In this experimental study we aim to assess how many function evaluations on the original problem are necessary to build a surrogate model endowed with the characteristics of the original problem and to develop a methodology that measures to which extent such a matching has been achieved. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1830483.1830558 | GECCO |
Keywords | Field | DocType |
real-worlds problem,case-specific tuning,optimization algorithm,surrogate problem,parameter tuning,best parameter,surrogate model,function evaluation,computationally cheap,extensive parameter,original problem,surrogate modeling,real-world problem,evolutionary algorithm,objective function | Mathematical optimization,Evolutionary algorithm,Computer science,Surrogate model,Optimization algorithm,Artificial intelligence,Almost surely,Machine learning,Metaheuristic | Conference |
Citations | PageRank | References |
6 | 0.67 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Preuss Mike | 1 | 933 | 81.70 |
| Günter Rudolph | 2 | 219 | 48.59 |
| Simon Wessing | 3 | 87 | 8.05 |