544 citations · 2.2k across the 29 of their papers we have counts for
22 papers · 1 filter
Microcosmos: Reimagining Artificial Life for the GPU Era
Mark Tensen, Ciaran Regan, Bert Wang-Chak Chan +3
Most artificial life simulators either operate on abstract substrates disconnected from physical reality, or simulate physically grounded worlds that do not scale to the population…
Evolution through Large Models
Joel Lehman, Jonathan Gordon, Shawn Jain +3
This paper pursues the insight that large language models (LLMs) trained to generate code can vastly improve the effectiveness of mutation operators applied to programs in genetic…
Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions
Rui Wang, Joel Lehman, Aditya Rawal +4
Creating open-ended algorithms, which generate their own never-ending stream of novel and appropriately challenging learning opportunities, could help to automate and accelerate pr…
Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity
Thomas Miconi, Aditya Rawal, Jeff Clune +1
The impressive lifelong learning in animal brains is primarily enabled by plastic changes in synaptic connectivity. Importantly, these changes are not passive, but are actively con…
Deep Innovation Protection: Confronting the Credit Assignment Problem in Training Heterogeneous Neural Architectures
Sebastian Risi, Kenneth O. Stanley
Deep reinforcement learning approaches have shown impressive results in a variety of different domains, however, more complex heterogeneous architectures such as world models requi…
An Overview of Open-Ended Evolution: Editorial Introduction to the Open-Ended Evolution II Special Issue
Norman Packard, Mark A. Bedau, Alastair Channon +4
Nature's spectacular inventiveness, reflected in the enormous diversity of form and function displayed by the biosphere, is a feature of life that distinguishes living most strongl…
Evolvability ES: Scalable and Direct Optimization of Evolvability
Alexander Gajewski, Jeff Clune, Kenneth O. Stanley +1
Designing evolutionary algorithms capable of uncovering highly evolvable representations is an open challenge; such evolvability is important because it accelerates evolution and e…
Deep Neuroevolution of Recurrent and Discrete World Models
Sebastian Risi, Kenneth O. Stanley
Neural architectures inspired by our own human cognitive system, such as the recently introduced world models, have been shown to outperform traditional deep reinforcement learning…
Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions
Rui Wang, Joel Lehman, Jeff Clune +1
While the history of machine learning so far largely encompasses a series of problems posed by researchers and algorithms that learn their solutions, an important question is wheth…
VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution
Rui Wang, Jeff Clune, Kenneth O. Stanley
Recent advances in deep neuroevolution have demonstrated that evolutionary algorithms, such as evolution strategies (ES) and genetic algorithms (GA), can scale to train deep neural…
Differentiable plasticity: training plastic neural networks with backpropagation
Thomas Miconi, Jeff Clune, Kenneth O. Stanley
How can we build agents that keep learning from experience, quickly and efficiently, after their initial training? Here we take inspiration from the main mechanism of learning in b…
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
Joel Lehman, Jeff Clune, Dusan Misevic +50
Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them. However, because evolution is an algorithmic pr…
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…
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…
A Proposed Infrastructure for Adding Online Interaction to Any Evolutionary Domain
Paul Szerlip, Kenneth O. Stanley
To address the difficulty of creating online collaborative evolutionary systems, this paper presents a new prototype library called Worldwide Infrastructure for Neuroevolution (WIN…
Unsupervised Feature Learning through Divergent Discriminative Feature Accumulation
Paul A. Szerlip, Gregory Morse, Justin K. Pugh +1
Unlike unsupervised approaches such as autoencoders that learn to reconstruct their inputs, this paper introduces an alternative approach to unsupervised feature learning called di…
Evolvability Is Inevitable: Increasing Evolvability Without the Pressure to Adapt
Joel Lehman, Kenneth O. Stanley
Why evolvability appears to have increased over evolutionary time is an important unresolved biological question. Unlike most candidate explanations, this paper proposes that incre…
Exploring Promising Stepping Stones by Combining Novelty Search with Interactive Evolution
Brian G. Woolley, Kenneth O. Stanley
The field of evolutionary computation is inspired by the achievements of natural evolution, in which there is no final objective. Yet the pursuit of objectives is ubiquitous in sim…