Abstract
Population-based Algorithm Portfolios (PAP) is an appealing framework for integrating different Evolutionary Algorithms (EAs) to solve challenging numerical optimization problems. Particularly, PAP has shown significant advantages to single EAs when a number of problems need to be solved simultaneously. Previous investigation on PAP reveals that choosing appropriate constituent algorithms is crucial to the success of PAP. However, no method has been developed for this purpose. In this paper, an extended version of PAP, namely PAP based on Estimated Performance Matrix (EPM-PAP) is proposed. EPM-PAP is equipped with a novel constituent algorithms selection module, which is based on the EPM of each candidate EAs. Empirical studies demonstrate that the EPM-based selection method can successfully identify appropriate constituent EAs, and thus EPM-PAP outperformed all single EAs considered in this work. © 2014 Elsevier Inc. All rights reserved.
| Original language | English |
|---|---|
| Pages (from-to) | 94-104 |
| Number of pages | 11 |
| Journal | Information Sciences |
| Volume | 279 |
| Early online date | 3 Apr 2014 |
| DOIs | |
| Publication status | Published - Sept 2014 |
| Externally published | Yes |
Funding
This work was supported in part by the 973 Program of China under Grant 2011CB707006, the National Natural Science Foundation of China under Grants 61175065 and 61329302, the Program for New Century Excellent Talents in University under Grant NCET-12-0512, the Science and Technological Fund of Anhui Province for Outstanding Youth under Grant 1108085J16, the EPSRC under Grant No. EP/J017515/1, and the European Union Seventh Framework Programme under Grant 247619. Xin Yao was supported by a Royal Society Wolfson Research Merit Award.
Keywords
- Algorithm subset selection
- Evolutionary optimization
- Global optimization
- Population-based Algorithm Portfolios