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A Melting Pot of Evolution and Learning

arXiv 2306.04971 ↗First posted 8 Jun 2023cs.NE cs.LGcited by 0

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Abstract (arXiv)

We survey eight recent works by our group, involving the successful blending of evolutionary algorithms with machine learning and deep learning: 1. Binary and Multinomial Classification through Evolutionary Symbolic Regression, 2. Classy Ensemble: A Novel Ensemble Algorithm for Classification, 3. EC-KitY: Evolutionary Computation Tool Kit in Python, 4. Evolution of Activation Functions for Deep Learning-Based Image Classification, 5. Adaptive Combination of a Genetic Algorithm and Novelty Search for Deep Neuroevolution, 6. An Evolutionary, Gradient-Free, Query-Efficient, Black-Box Algorithm for Generating Adversarial Instances in Deep Networks, 7. Foiling Explanations in Deep Neural Networks, 8. Patch of Invisibility: Naturalistic Black-Box Adversarial Attacks on Object Detectors.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 50 references that are in the map. First posted on arXiv 8 Jun 2023.

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References · 25

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  3. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,455
  4. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,315
  5. 2016Practical Black-Box Attacks against Machine Learningcited by 893
  6. 2015The Limitations of Deep Learning in Adversarial Settingscited by 784
  7. 2017Evolution Strategies as a Scalable Alternative to Reinforcement Learningcited by 555
  8. 2018Audio Adversarial Examples: Targeted Attacks on Speech-to-Textcited by 268
  9. 2017Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learningcited by 148
  10. 2014Towards Deep Neural Network Architectures Robust to Adversarial Examplescited by 146
  11. 2018Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hidingcited by 83
  12. 2014Learning Activation Functions to Improve Deep Neural Networkscited by 62
13 more
  1. 2020TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLPcited by 45
  2. 2019When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signaturescited by 40
  3. 2022Foiling Explanations in Deep Neural Networkscited by 14
  4. 2022An Evolutionary, Gradient-Free, Query-Efficient, Black-Box Algorithm for Generating Adversarial Instances in Deep Networkscited by 11
  5. 2022Evolution of Activation Functions for Deep Learning-Based Image Classificationcited by 6
  6. 2022Symbolic-Regression Boostingcited by 4
  7. 2022Neural Networks with A La Carte Selection of Activation Functionscited by 4
  8. 2022EC-KitY: Evolutionary Computation Tool Kit in Python with Seamless Machine Learning Integrationcited by 4
  9. 2022Binary and Multinomial Classification through Evolutionary Symbolic Regressioncited by 3
  10. 2019Evolution of Novel Activation Functions in Neural Network Training with Applications to Classification of Exoplanetscited by 2
  11. 2022Combining Deep Learning with Good Old-Fashioned Machine Learningcited by 2
  12. 2022Adaptive Combination of a Genetic Algorithm and Novelty Search for Deep Neuroevolutioncited by 2
  13. 2023Classy Ensemble: A Novel Ensemble Algorithm for Classificationcited by 1

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