A problem configuration study of the robustness of a black-box search algorithm hyper-heuristic

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{Martin:2014:GECCOcompa,

• author = "Matthew A. Martin and Daniel R. Tauritz",
• title = "A problem configuration study of the robustness of a black-box search algorithm hyper-heuristic",
• booktitle = "GECCO 2014 4th workshop on evolutionary computation for the automated design of algorithms",
• year = "2014",
• editor = "John Woodward and Jerry Swan and Earl Barr",
• isbn13 = "978-1-4503-2881-4",
• keywords = "genetic algorithms, genetic programming",
• pages = "1389--1396",
• month = "12-16 " # jul,
• organisation = "SIGEVO",
• address = "Vancouver, BC, Canada",
• URL = "http://doi.acm.org/10.1145/2598394.2609872",
• DOI = "doi:10.1145/2598394.2609872",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• 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-specialisation. This paper presents a study on the second generation hyper-heuristic which employs a multi-sample training approach to alleviate the over-specialisation 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.",
• notes = "Also known as \cite{2609872} Distributed at GECCO-2014.",

}

Genetic Programming entries for Matthew A Martin Daniel R Tauritz

Citations