544 citations · 2.2k across the 29 of their papers we have counts for
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On the Relationship Between the OpenAI Evolution Strategy and Stochastic Gradient Descent
Xingwen Zhang, Jeff Clune, Kenneth O. Stanley
Because stochastic gradient descent (SGD) has shown promise optimizing neural networks with millions of parameters and few if any alternatives are known to exist, it has moved to t…
ES Is More Than Just a Traditional Finite-Difference Approximator
Joel Lehman, Jay Chen, Jeff Clune +1
An evolution strategy (ES) variant based on a simplification of a natural evolution strategy recently attracted attention because it performs surprisingly well in challenging deep…
Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
Felipe Petroski Such, Vashisht Madhavan, Edoardo Conti +3
Deep artificial neural networks (DNNs) are typically trained via gradient-based learning algorithms, namely backpropagation. Evolution strategies (ES) can rival backprop-based algo…
Safe Mutations for Deep and Recurrent Neural Networks through Output Gradients
Joel Lehman, Jay Chen, Jeff Clune +1
While neuroevolution (evolving neural networks) has a successful track record across a variety of domains from reinforcement learning to artificial life, it is rarely applied to la…
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents
Edoardo Conti, Vashisht Madhavan, Felipe Petroski Such +3
Evolution strategies (ES) are a family of black-box optimization algorithms able to train deep neural networks roughly as well as Q-learning and policy gradient methods on challeng…
The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System
Joost Huizinga, Kenneth O. Stanley, Jeff Clune
Natural evolution has produced a tremendous diversity of functional organisms. Many believe an essential component of this process was the evolution of evolvability, whereby evolut…
Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks
Andrea Soltoggio, Kenneth O. Stanley, Sebastian Risi
Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements…