A Problem Configuration Study of the Robustness of a Black-box Search Algorithm Hyper-heuristic
Abstract
Black-Box Search Algorithms (BBSAs) tailored to a specific problem class may be expected to significantly outperform more general purpose problem solvers, including canonical evolutionary algorithms. Recent work has introduced a novel approach to evolving tailored BBSAs through a genetic programming hyper-heuristic. However, that first generation of hyper-heuristics suffered from over-specialization. This paper presents a study on the second generation hyperheuristic which employs a multi-sample training approach to alleviate the over-specialization problem. In particular, the study is focused on the affect that the multi-sample approach has on the problem configuration landscape. A variety of experiments are reported on which demonstrate the significant increase in the robustness of the generated algorithms to changes in problem configuration due to the multi-sample approach. The results clearly show the resulting BBSAs' ability to outperform established BBSAs, including canonical evolutionary algorithms. The trade-off between a priori computational time and the generated algorithm robustness is investigated, demonstrating the performance gain possible given additional run-time.
Recommended Citation
M. A. Martin and D. R. Tauritz, "A Problem Configuration Study of the Robustness of a Black-box Search Algorithm Hyper-heuristic," Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion (GECCO 2014), pp. 1389 - 1396, Association for Computing Machinery (ACM), Jan 2014.
The definitive version is available at https://doi.org/10.1145/2598394.2609872
Meeting Name
16th Genetic and Evolutionary Computation Conference, GECCO 2014 (2014: Jul. 12-16, Vancouver, British Columbia, Canada)
Department(s)
Computer Science
Research Center/Lab(s)
Center for High Performance Computing Research
Sponsor(s)
Missouri University of Science and Technology. Natural Computation Laboratory
Keywords and Phrases
Black-Box Search Algorithms; Evolutionary Algorithms; Genetic Programming; Hyper-Heuristics
International Standard Book Number (ISBN)
978-1450328814
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2014 Association for Computing Machinery (ACM), All rights reserved.
Publication Date
01 Jan 2014
