Paper ID: 2407.10477

Deep Learning-Based Operators for Evolutionary Algorithms

Eliad Shem-Tov, Moshe Sipper, Achiya Elyasaf

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.

Submitted: Jul 15, 2024