| Texto completo | |
| Autor(es): |
Imai Aldeia, Guilherme Seidyo
;
de Franca, Fabricio Olivetti
;
La Cava, William G.
Número total de Autores: 3
|
| Tipo de documento: | Artigo Científico |
| Fonte: | PROCEEDINGS OF THE 2024 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, GECCO 2024; v. N/A, p. 9-pg., 2024-01-01. |
| Resumo | |
Parent selection plays an important role in evolutionary algorithms, and many strategies exist to select the parent pool before breeding the next generation. Methods often rely on average error over the entire dataset as a criterion to select the parents, which can lead to an information loss due to aggregation of all test cases. Under epsilon-lexicase selection, the population goes to a selection pool that is iteratively reduced by using each test individually, discarding individuals with an error higher than the elite error plus the median absolute deviation (MAD) of errors for that particular test case. In an attempt to better capture differences in performance of individuals on cases, we propose a new criteria that splits errors into two partitions that minimize the total variance within partitions. Our method was embedded into the FEAT symbolic regression algorithm, and evaluated with the SRBench framework, containing 122 black-box synthetic and real-world regression problems. The empirical results show a better performance of our approach compared to traditional epsilon-lexicase selection in the real-world datasets while showing equivalent performance on the synthetic dataset. (AU) | |
| Processo FAPESP: | 21/12706-1 - Regressão simbólica com restrição de forma da função |
| Beneficiário: | Fabricio Olivetti de França |
| Modalidade de apoio: | Bolsas no Exterior - Pesquisa |