Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression
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| Názov: | Active Learning in Genetic Programming: Guiding Efficient Data Collection for Symbolic Regression |
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| Autori: | Nathan Haut, Wolfgang Banzhaf, Bill Punch |
| Zdroj: | IEEE Transactions on Evolutionary Computation. 29:1100-1111 |
| Publication Status: | Preprint |
| Informácie o vydavateľovi: | Institute of Electrical and Electronics Engineers (IEEE), 2025. |
| Rok vydania: | 2025 |
| Predmety: | FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing, Neural and Evolutionary Computing (cs.NE), Machine Learning (cs.LG) |
| Popis: | This paper examines various methods of computing uncertainty and diversity for active learning in genetic programming. We found that the model population in genetic programming can be exploited to select informative training data points by using a model ensemble combined with an uncertainty metric. We explored several uncertainty metrics and found that differential entropy performed the best. We also compared two data diversity metrics and found that correlation as a diversity metric performs better than minimum Euclidean distance, although there are some drawbacks that prevent correlation from being used on all problems. Finally, we combined uncertainty and diversity using a Pareto optimization approach to allow both to be considered in a balanced way to guide the selection of informative and unique data points for training. |
| Druh dokumentu: | Article |
| ISSN: | 1941-0026 1089-778X |
| DOI: | 10.1109/tevc.2024.3471341 |
| DOI: | 10.48550/arxiv.2308.00672 |
| Prístupová URL adresa: | http://arxiv.org/abs/2308.00672 |
| Rights: | CC BY |
| Prístupové číslo: | edsair.doi.dedup.....99e84dae06c82d0d9b16e23ff76e01a3 |
| Databáza: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstrakt: | This paper examines various methods of computing uncertainty and diversity for active learning in genetic programming. We found that the model population in genetic programming can be exploited to select informative training data points by using a model ensemble combined with an uncertainty metric. We explored several uncertainty metrics and found that differential entropy performed the best. We also compared two data diversity metrics and found that correlation as a diversity metric performs better than minimum Euclidean distance, although there are some drawbacks that prevent correlation from being used on all problems. Finally, we combined uncertainty and diversity using a Pareto optimization approach to allow both to be considered in a balanced way to guide the selection of informative and unique data points for training. |
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| ISSN: | 19410026 1089778X |
| DOI: | 10.1109/tevc.2024.3471341 |