Multi-sample Evolution of Robust Black-Box Search Algorithms
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 overspecialization. This poster paper presents a second generation hyperheuristic employing a multi-sample training approach to alleviate the overspecialization problem. A variety of experiments demonstrated the significant increase in the robustness of the generated algorithms due to the multi-sample approach, clearly showing its ability to outperform established BBSAs. The trade-off between a priori computational time and the generated algorithm robustness is investigated, demonstrating the performance gain possible given additional run-time.
M. A. Martin and D. R. Tauritz, "Multi-sample Evolution of Robust Black-Box Search Algorithms," Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion (GECCO 2014), pp. 195-196, Association for Computing Machinery (ACM), Jan 2014.
The definitive version is available at https://doi.org/10.1145/2598394.2598448
16th Genetic and Evolutionary Computation Conference, GECCO 2014 (2014: Jul. 12-16, Vancouver, British Columbia, Canada)
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)
Article - Conference proceedings
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