Deep Learning-Based Operators for Evolutionary Algorithms
<|reference_start|>Deep Learning-Based Operators for Evolutionary Algorithms: 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.<|reference_end|>
@article{shem-tov2024deep,
    title={Deep Learning-Based Operators for Evolutionary Algorithms},
    author={Eliad Shem-Tov, Moshe Sipper, Achiya Elyasaf},
    journal={arXiv preprint arXiv:2407.10477},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10477},
    primaryClass={cs.NE cs.LG}
}