Title

Evolving Black-box Search Algorithms Employing Genetic Programming

Abstract

Restricting the class of problems we want to perform well on allows Black Box Search Algorithms (BBSAs) specifi- cally tailored to that class to significantly outperform more general purpose problem solvers. However, the fields that encompass BBSAs, including Evolutionary Computing, are mostly focused on improving algorithm performance over increasingly diversified problem classes. By definition, the payoff for designing a high quality general purpose solver is far larger in terms of the number of problems it can ad- dress, than a specialized BBSA. This paper introduces a novel approach to creating tailored BBSAs through auto- mated design employing genetic programming. An exper- iment is reported which demonstrates its ability to create novel BBSAs which outperform established BBSAs includ- ing canonical evolutionary algorithms.

Meeting Name

15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013 (2013: Jul. 6-10, Amsterdam, The Netherlands)

Department(s)

Computer Science

Research Center/Lab(s)

Missouri University of Science and Technology. Natural Computation Laboratory

Keywords and Phrases

Black-Box Search Algorithms; Evolutionary Algorithms; Genetic Programming

International Standard Book Number (ISBN)

9781450319645

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2013 Association for Computing Machinery (ACM), All rights reserved.


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