Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
7-2014
Abstract
The present study introduces an automated mechanism to build algorithm portfolios for memetic algorithms. The objective is to determine an algorithm set involving combinations of crossover, mutation and local search operators based on their past performance. The past performance is used to cluster algorithm combinations. Top performing combinations are then considered as the members of the set. The set is expected to have algorithm combinations complementing each other with respect to their strengths in a portfolio setting. In other words, each algorithm combination should be good at solving a certain type of problem instances such that this set can be used to solve different problem instances. The set is used together with an online selection strategy. An empirical analysis is performed on the Quadratic Assignment problem to show the advantages of the proposed approach.
Discipline
Artificial Intelligence and Robotics | Theory and Algorithms
Publication
GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, July 12-16, 2014, Vancouver, BC, Canada
First Page
197
Last Page
198
ISBN
9781450328814
Identifier
10.1145/2598394.2598455
Publisher
ACM
City or Country
New York
Citation
MISIR, Mustafa; HANDOKO, Stephanus Daniel; and LAU, Hoong Chuin.
Building algorithm portfolios for memetic algorithms. (2014). GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, July 12-16, 2014, Vancouver, BC, Canada. 197-198.
Available at: https://ink.library.smu.edu.sg/sis_research/2665
Copyright Owner and License
LARC
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1145/2598394.2598455