A sequential algorithm portfolio approach for black box optimization
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 559-570 |
| Journal / Publication | Swarm and Evolutionary Computation |
| Volume | 44 |
| Online published | 1 Aug 2018 |
| Publication status | Published - Feb 2019 |
Link(s)
Abstract
A large number of optimization algorithms have been proposed. However, the no free lunch (NFL) theorems inform us that no algorithm can solve all types of optimization problems. An approach, which can suggest the most suitable algorithm for different types of problems, is valuable. In this paper, we propose an approach called sequential algorithm portfolio (SAP) which belongs to the inter-disciplinary fields of algorithm portfolio and algorithm selection. It uses a pre-trained predictor to predict the most suitable algorithm and a termination mechanism to automatically stop the optimization algorithms. The SAP is easy to implement and can incorporate any optimization algorithm. We experimentally compare SAP with two state-of-the-art algorithm portfolio approaches and single optimization algorithms. The result shows that SAP is a well-performing algorithm portfolio approach.
Research Area(s)
- Algorithm portfolio, Algorithm selection, Heuristic algorithms, Optimization problems, Performance prediction
Citation Format(s)
A sequential algorithm portfolio approach for black box optimization. / He, Yaodong; Yuen, Shiu Yin; Lou, Yang et al.
In: Swarm and Evolutionary Computation, Vol. 44, 02.2019, p. 559-570.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
