Name
Abstract | ||
|---|---|---|
Automatic algorithm configurators are important practical tools for improving program performance measures, such as solution time or prediction accuracy. Local search approaches in particular have proven very effective for tuning algorithms. In sequential local search, the use of predictive models has proven beneficial for obtaining good tuning results. We study the use of non-parametric models in the context of population-based algorithm configurators. We introduce a new model designed specifically for the task of predicting high-performance regions in the parameter space. Moreover, we introduce the ideas of genetic engineering of offspring as well as sexual selection of parents. Numerical results show that model-based genetic algorithms significantly improve our ability to effectively configure algorithms automatically. |
Year | Venue | Field |
|---|---|---|
2015 | IJCAI | Population,Computer science,Algorithm configuration,Artificial intelligence,Parameter space,Local search (optimization),Machine learning,Genetic algorithm |
DocType | Citations | PageRank |
Conference | 10 | 0.57 |
References | Authors | |
11 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlos Ansotegui | 1 | 117 | 9.84 |
| Yuri Malitsky | 2 | 278 | 17.79 |
| Horst Samulowitz | 3 | 316 | 26.05 |
| Meinolf Sellmann | 4 | 728 | 48.77 |
| Kevin Tierney | 5 | 14 | 1.69 |