Evolutionary Algorithms

192 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

Progressive Neural Architecture Search

tensorflow/models ECCV 2018

We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionary algorithms.

Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

uber-research/deep-neuroevolution 18 Dec 2017

Here we demonstrate they can: we evolve the weights of a DNN with a simple, gradient-free, population-based genetic algorithm (GA) and it performs well on hard deep RL problems, including Atari and humanoid locomotion.

Evolution-Guided Policy Gradient in Reinforcement Learning

ShawK91/erl_paper_nips18 NeurIPS 2018

However, these methods typically suffer from three core difficulties: temporal credit assignment with sparse rewards, lack of effective exploration, and brittle convergence properties that are extremely sensitive to hyperparameters.

Neural Architecture Optimization

renqianluo/NAO NeurIPS 2018

The performance predictor and the encoder enable us to perform gradient based optimization in the continuous space to find the embedding of a new architecture with potentially better accuracy.

Tree Search vs Optimization Approaches for Map Generation

amidos2006/gym-pcgrl 27 Mar 2019

We compare them on three different game level generation problems: Binary, Zelda, and Sokoban.

Regularized Evolution for Image Classifier Architecture Search

tensorflow/tpu 5 Feb 2018

The effort devoted to hand-crafting neural network image classifiers has motivated the use of architecture search to discover them automatically.

GenAttack: Practical Black-box Attacks with Gradient-Free Optimization

nesl/adversarial_genattack 28 May 2018

Our experiments on different datasets (MNIST, CIFAR-10, and ImageNet) show that GenAttack can successfully generate visually imperceptible adversarial examples against state-of-the-art image recognition models with orders of magnitude fewer queries than previous approaches.

IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics

IOHprofiler/IOHAnalyzer 8 Jul 2020

An R programming interface is provided for users preferring to have a finer control over the implemented functionalities.

Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images

Evolving-AI-Lab/fooling CVPR 2015

Here we show a related result: it is easy to produce images that are completely unrecognizable to humans, but that state-of-the-art DNNs believe to be recognizable objects with 99. 99% confidence (e. g. labeling with certainty that white noise static is a lion).

Large-Scale Evolution of Image Classifiers

marijnvk/LargeScaleEvolution ICML 2017

Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone.