Deep Learning-Based Operators for Evolutionary Algorithms
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916324326768640 |
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| author | Shem-Tov, Eliad Sipper, Moshe Elyasaf, Achiya |
| author_facet | Shem-Tov, Eliad Sipper, Moshe Elyasaf, Achiya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_10477 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning-Based Operators for Evolutionary Algorithms Shem-Tov, Eliad Sipper, Moshe Elyasaf, Achiya Neural and Evolutionary Computing Machine Learning 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. |
| title | Deep Learning-Based Operators for Evolutionary Algorithms |
| topic | Neural and Evolutionary Computing Machine Learning |
| url | https://arxiv.org/abs/2407.10477 |