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Optimization of Discrete Parameters Using the Adaptive Gradient Method and Directed Evolution

arXiv 2401.06834 ↗First posted 12 Jan 2024math.OC cs.LG cs.NEcited by 0

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Abstract (arXiv)

The problem is considered of optimizing discrete parameters in the presence of constraints. We use the stochastic sigmoid with temperature and put forward the new adaptive gradient method CONGA. The search for an optimal solution is carried out by a population of individuals. Each of them varies according to gradients of the 'environment' and is characterized by two temperature parameters with different annealing schedules. Unadapted individuals die, and optimal ones interbreed, the result is directed evolutionary dynamics. The proposed method is illustrated using the well-known combinatorial problem for optimal packing of a backpack (0-1 KP).

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 21 references that are in the map. First posted on arXiv 12 Jan 2024.

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14 of 21 references.

References · 14

  1. 2017Neural Discrete Representation Learningcited by 4,650
  2. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  3. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,708
  4. 2021BEiT: BERT Pre-Training of Image Transformerscited by 2,360
  5. 2013Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computationcited by 2,031
  6. 2016The Concrete Distribution: A Continuous Relaxation of Discrete Random Variablescited by 1,135
  7. 2015Pointer Networkscited by 882
  8. 2018MolGAN: An implicit generative model for small molecular graphscited by 392
  9. 2020Sparse Sinkhorn Attentioncited by 250
  10. 2017Generating Multi-label Discrete Patient Records using Generative Adversarial Networkscited by 197
  11. 2018Learning Latent Permutations with Gumbel-Sinkhorn Networkscited by 172
  12. 2016Hierarchical Multiscale Recurrent Neural Networkscited by 102
2 more
  1. 2021A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learningcited by 82
  2. 2022Features for the 0-1 knapsack problem based on inclusionwise maximal solutionscited by 1

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