Deep Learning-Based Operators for Evolutionary Algorithms

Eliad Shem-Tov; Moshe Sipper; Achiya Elyasaf

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arXiv · 2407.10477

Deep Learning-Based Operators for Evolutionary Algorithms

Eliad Shem-Tov·Moshe Sipper·Achiya Elyasaf

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Abstract

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.

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Eliad Shem-Tov, Moshe Sipper, Achiya Elyasaf. 2024-07-15. Deep Learning-Based Operators for Evolutionary Algorithms. https://arxiv.org/abs/2407.10477

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