Deep Neural Crossover: A Multi-Parent Operator That Leverages Gene Correlations

Eliad Shem-Tov, Achiya Elyasaf

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present a novel multi-parent crossover operator in genetic algorithms (GAs) called "Deep Neural Crossover"(DNC). Unlike conventional GA crossover operators that rely on a random selection of parental genes, DNC leverages the capabilities of deep reinforcement learning (DRL) and an encoder-decoder architecture to select the genes. Specifically, we use DRL to learn a policy for selecting promising genes. The policy is stochastic, to maintain the stochastic nature of GAs, representing a distribution for selecting genes with a higher probability of improving fitness. Our architecture features a recurrent neural network (RNN) to encode the parental genomes into latent memory states, and a decoder RNN that utilizes an attention-based pointing mechanism to generate a distribution over the next selected gene in the offspring. The operator's architecture is designed to find linear and nonlinear correlations between genes and translate them to gene selection. To reduce computational cost, we present a transfer-learning approach, wherein the architecture is initially trained on a single problem within a specific domain and then applied to solving other problems of the same domain. We compare DNC to known operators from the literature over two benchmark domains, outperforming all baselines.

Original languageEnglish
Title of host publicationGECCO 2024 - Proceedings of the 2024 Genetic and Evolutionary Computation Conference
PublisherAssociation for Computing Machinery, Inc
Pages1045-1053
Number of pages9
ISBN (Electronic)9798400704949
DOIs
StatePublished - 14 Jul 2024
Event2024 Genetic and Evolutionary Computation Conference, GECCO 2024 - Melbourne, Australia
Duration: 14 Jul 202418 Jul 2024

Publication series

NameGECCO 2024 - Proceedings of the 2024 Genetic and Evolutionary Computation Conference

Conference

Conference2024 Genetic and Evolutionary Computation Conference, GECCO 2024
Country/TerritoryAustralia
CityMelbourne
Period14/07/2418/07/24

Keywords

  • combinatorial optimization
  • genetic algorithm
  • recombination operator
  • reinforcement learning
  • surrogate model

ASJC Scopus subject areas

  • Logic
  • Software
  • Control and Optimization
  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Science Applications

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