Evolving Black-box Search Algorithms Employing Genetic Programming
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.
M. A. Martin and D. R. Tauritz, "Evolving Black-box Search Algorithms Employing Genetic Programming," Proceedings of the 2013 Genetic and Evolutionary Computation Conference Companion (GECCO 2013), pp. 1497-1504, Association for Computing Machinery (ACM), Jan 2013.
The definitive version is available at https://doi.org/10.1145/2464576.2482728
15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013 (2013: Jul. 6-10, Amsterdam, The Netherlands)
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)
Article - Conference proceedings
© 2013 Association for Computing Machinery (ACM), All rights reserved.
01 Jan 2013