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| Content Provider | ACM Digital Library |
|---|---|
| Author | Tauritz, Daniel R. Martin, Matthew A. |
| Abstract | Restricting the class of problems we want to perform well on allows Black Box Search Algorithms (BBSAs) specifically 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 address, than a specialized BBSA. This paper introduces a novel approach to creating tailored BBSAs through automated design employing genetic programming. An experiment is reported which demonstrates its ability to create novel BBSAs which outperform established BBSAs including canonical evolutionary algorithms. |
| Starting Page | 1497 |
| Ending Page | 1504 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450319645 |
| DOI | 10.1145/2464576.2482728 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-07-06 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Genetic programming Black-box search algorithms Evolutionary algorithms |
| Content Type | Text |
| Resource Type | Article |
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