Multi-sample Evolution of Robust Black-Box Search Algorithms

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 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.

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

Link to Full Text

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