تقرير
Deep Learning-Based Operators for Evolutionary Algorithms
| العنوان: | Deep Learning-Based Operators for Evolutionary Algorithms |
|---|---|
| المؤلفون: | Shem-Tov, Eliad, Sipper, Moshe, Elyasaf, Achiya |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Neural and Evolutionary Computing, Computer Science - Machine Learning |
| الوصف: | We present two novel domain-independent genetic operators that harness the capabilities of deep learning: a crossover operator for genetic algorithms and a mutation operator for genetic programming. Deep Neural Crossover leverages the capabilities of deep reinforcement learning and an encoder-decoder architecture to select offspring genes. BERT mutation masks multiple gp-tree nodes and then tries to replace these masks with nodes that will most likely improve the individual's fitness. We show the efficacy of both operators through experimentation. Comment: 16 pages, 7 figures, 2 tables. Accepted to Genetic Programming Theory & Practice XXI (GPTP 2024). arXiv admin note: text overlap with arXiv:2403.11159 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.10477 |
| رقم الأكسشن: | edsarx.2407.10477 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |