Evolutionary Algorithms
229 papers with code • 0 benchmarks • 0 datasets
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Progressive Neural Architecture Search
tensorflow/models
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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
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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
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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.
Regularized Evolution for Image Classifier Architecture Search
tensorflow/tpu
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The effort devoted to hand-crafting neural network image classifiers has motivated the use of architecture search to discover them automatically.
Neural Architecture Optimization
renqianluo/NAO
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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
We compare them on three different game level generation problems: Binary, Zelda, and Sokoban.
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization
nesl/adversarial_genattack
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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
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
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
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ICML 2017
Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone.
