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| Content Provider | ACM Digital Library |
|---|---|
| Author | Galván López, Edgar Trujillo, Leonardo Legrand, Pierrick Martínez, Yuliana |
| Abstract | An open question within Genetic Programming (GP) is how to characterize problemdifficulty. The goal is to develop predictive tools that estimate how difficult a problemis for GP to solve. Here we consider two groups of methods. We call the first group Evolvability Indicators (EI), measures that capture how amendable the fitness landscape is to a GP search. Examples of EIs are Fitness Distance Correlation (FDC) and Negative Slope Coefficient (NSC). The second group are Predictors of Expected Performance (PEP), models that take as input a set of descriptive attributes of a problem and predict the expected performance of GP. This paper compares an EI, the NSC, and a PEP model for a GP classifier. Results suggest that the EI does not correlate with the performance of the GP classifiers. Conversely, the PEP models show a high correlation with GP performance. |
| Starting Page | 1489 |
| Ending Page | 1490 |
| Page Count | 2 |
| File Format | |
| ISBN | 9781450311786 |
| DOI | 10.1145/2330784.2331006 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-07-07 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Genetic programming Classification Performance prediction |
| Content Type | Text |
| Resource Type | Article |
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