mlsanityData to 14 Oct 2026

Deep Learning-Based Operators for Evolutionary Algorithms

arXiv 2407.10477 ↗First posted 15 Jul 2024cs.NE cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 29 references that are in the map. First posted on arXiv 15 Jul 2024.

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7 of 29 references.

References · 7

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2015Pointer Networkscited by 895
  3. 2019SpanBERT: Improving Pre-training by Representing and Predicting Spanscited by 834
  4. 2016Neural Combinatorial Optimization with Reinforcement Learningcited by 608
  5. 2022Evolution through Large Modelscited by 134
  6. 2020Evolving Context-Aware Recommender Systems With Users in Mindcited by 5
  7. 2024Deep Neural Crossovercited by 2

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  1. 2025Neural Genetic Search in Discrete Spaces
  2. 2025Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization
  3. 2026Genetic Algorithms with Optimization Guided Operators
  4. 2026AVO: Agentic Variation Operators for Autonomous Evolutionary Search
  5. 2026Decoupling Numerical and Structural Parameters: An Empirical Study on Adaptive Genetic Algorithms via Deep Reinforcement Learning for the Large-Scale TSP
  6. 2026Discovering Interpretable Multi-Parameter Control Policies for Evolutionary Algorithms Using Deep Reinforcement Learning
  7. 2024Auto-configuring Exploration-Exploitation Tradeoff in Evolutionary Computation via Deep Reinforcement Learning
  8. 2026Evolutionary Soups: Evolving Mixture-of-Experts for Multi-Objective LLM Alignment