# A Melting Pot of Evolution and Learning

arXiv: https://arxiv.org/abs/2306.04971 · HTML: https://mlsanity.com/paper/2306.04971/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 50 references that are in the map.
- First posted on arXiv 8 Jun 2023.

## Builds on (most cited first, 25 of 25)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Practical Black-Box Attacks against Machine Learning](https://mlsanity.com/paper/1602.02697/) (2016)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [Audio Adversarial Examples: Targeted Attacks on Speech-to-Text](https://mlsanity.com/paper/1801.01944/) (2018)
- [Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning](https://mlsanity.com/paper/1712.06567/) (2017)
- [Towards Deep Neural Network Architectures Robust to Adversarial Examples](https://mlsanity.com/paper/1412.5068/) (2014)
- [Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hiding](https://mlsanity.com/paper/1808.05665/) (2018)
- [Learning Activation Functions to Improve Deep Neural Networks](https://mlsanity.com/paper/1412.6830/) (2014)
- [TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP](https://mlsanity.com/paper/2005.05909/) (2020)
- [When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures](https://mlsanity.com/paper/1909.03418/) (2019)
- [Foiling Explanations in Deep Neural Networks](https://mlsanity.com/paper/2211.14860/) (2022)
- [An Evolutionary, Gradient-Free, Query-Efficient, Black-Box Algorithm for Generating Adversarial Instances in Deep Networks](https://mlsanity.com/paper/2208.08297/) (2022)
- [Evolution of Activation Functions for Deep Learning-Based Image Classification](https://mlsanity.com/paper/2206.12089/) (2022)
- [Symbolic-Regression Boosting](https://mlsanity.com/paper/2206.12082/) (2022)
- [Neural Networks with A La Carte Selection of Activation Functions](https://mlsanity.com/paper/2206.12166/) (2022)
- [EC-KitY: Evolutionary Computation Tool Kit in Python with Seamless Machine Learning Integration](https://mlsanity.com/paper/2207.10367/) (2022)
- [Binary and Multinomial Classification through Evolutionary Symbolic Regression](https://mlsanity.com/paper/2206.12706/) (2022)
- [Evolution of Novel Activation Functions in Neural Network Training with Applications to Classification of Exoplanets](https://mlsanity.com/paper/1906.01975/) (2019)
- [Combining Deep Learning with Good Old-Fashioned Machine Learning](https://mlsanity.com/paper/2207.03757/) (2022)
- [Adaptive Combination of a Genetic Algorithm and Novelty Search for Deep Neuroevolution](https://mlsanity.com/paper/2209.03618/) (2022)
- [Classy Ensemble: A Novel Ensemble Algorithm for Classification](https://mlsanity.com/paper/2302.10580/) (2023)

## 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.