A Bayesian Restarting Approach to Algorithm Selection
Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with ISBN/ISSN) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | Simulated Evolution and Learning |
| Editors | Yuhui Shi, Kay Chen Tan, Mengjie Zhang, Ke Tang, Xiaodong Li, Qingfu Zhang, Ying Tan, Martin Middendorf, Yaochu Jin |
| Publisher | Springer, Cham |
| Pages | 397-408 |
| ISBN (Electronic) | 978-3-319-68759-9 |
| ISBN (Print) | 978-3-319-68758-2 |
| Publication status | Published - Nov 2017 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Publisher | Springer |
| Volume | 10593 |
| ISSN (Print) | 0302-9743 |
Conference
| Title | 11th International Conference on Simulated Evolution and Learning ( SEAL 2017) |
|---|---|
| Location | Southern University of Science and Technology |
| Place | China |
| City | Shenzhen |
| Period | 10 - 13 November 2017 |
Link(s)
Abstract
A Bayesian algorithm selection framework for black box optimization problems is proposed. A set of benchmark problems is used for training. The performance of a set of algorithms on the problems is recorded. In the beginning, an algorithm is randomly selected to run on the given unknown problem. A Bayesian approach is used to measure the similarity between problems. The most similar problem to the given problem is selected. Then the best algorithm for solving it is suggested for the second run. The process repeats until n algorithms have been run. The best solution out of n runs is recorded. We have experimentally evaluated the property and performance of the framework. Conclusions are (1) it can identify the most similar problem efficiently; (2) it benefits from a restart mechanism. It performs better when more knowledge is learned. Thus it is a good algorithm selection framework.
Research Area(s)
- Algorithm selection, Bayesian approach, Evolutionary algorithm, Monte Carlo method, Optimization problems
Citation Format(s)
A Bayesian Restarting Approach to Algorithm Selection. / He, Yaodong; Yuen, Shiu Yin; Lou, Yang.
Simulated Evolution and Learning. ed. / Yuhui Shi; Kay Chen Tan; Mengjie Zhang; Ke Tang; Xiaodong Li; Qingfu Zhang; Ying Tan; Martin Middendorf; Yaochu Jin. Springer, Cham, 2017. p. 397-408 (Lecture Notes in Computer Science; Vol. 10593).Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45) › 32_Refereed conference paper (with ISBN/ISSN) › peer-review
