A Problem Configuration Study of the Robustness of a Black-box Search Algorithm Hyper-heuristic


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.

Meeting Name

16th Genetic and Evolutionary Computation Conference, GECCO 2014 (2014: Jul. 12-16, Vancouver, British Columbia, Canada)


Computer Science

Research Center/Lab(s)

Center for High Performance Computing Research


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)


Document Type

Article - Conference proceedings

Document Version


File Type





© 2014 Association for Computing Machinery (ACM), All rights reserved.

Publication Date

01 Jan 2014