Kenneth O. Stanley

Evolving Neural Networks through Augmenting Topologies

Kenneth O. Stanley, Risto Miikkulainen
Evolutionary Computation
2002

An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT),… (More)

Abandoning Objectives: Evolution Through the Search for Novelty Alone

Joel Lehman, Kenneth O. Stanley
Evolutionary Computation
2011

In evolutionary computation, the fitness function normally measures progress toward an objective in the search space, effectively acting as an objective function. Through deception, such objective… (More)

Compositional pattern producing networks: A novel abstraction of development

Kenneth O. Stanley
Genetic Programming and Evolvable Machines
2007

Natural DNA can encode complexity on an enormous scale. Researchers are attempting to achieve the same representational efficiency in computers by implementing developmental encodings, i.e. encodings… (More)

A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks

Kenneth O. Stanley, David B. D'Ambrosio, Jason Gauci
Artificial Life
2009

Research in neuroevolutionthat is, evolving artificial neural networks (ANNs) through evolutionary algorithmsis inspired by the evolution of biological brains, which can contain trillions of… (More)

Exploiting Open-Endedness to Solve Problems Through the Search for Novelty

This paper establishes a link between the challenge of solving highly ambitious problems in machine learning and the goal of reproducing the dynamics of open-ended evolution in artificial life. A… (More)

Efficient Reinforcement Learning Through Evolving Neural Network Topologies

Neuroevolution is currently the strongest method on the pole-balancing benchmark reinforcement learning tasks. Although earlier studies suggested that there was an advantage in evolving the network… (More)

Efficient Evolution of Neural Networks through Complexification

Kenneth O. Stanley
2004

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Search-Based Procedural Content Generation: A Taxonomy and Survey

Julian Togelius, Georgios N. Yannakakis, Kenneth O. Stanley, Cameron Browne
IEEE Transactions on Computational Intelligence…
2011

The focus of this survey is on research in applying evolutionary and other metaheuristic search algorithms to automatically generating content for games, both digital and nondigital (such as board… (More)

Evolving a diversity of virtual creatures through novelty search and local competition

An ambitious challenge in artificial life is to craft an evolutionary process that discovers a wide diversity of well-adapted virtual creatures within a single run. Unlike in nature, evolving… (More)

A Taxonomy for Artificial Embryogeny

A major challenge for evolutionary computation is to evolve phenotypes such as neural networks, sensory systems, or motor controllers at the same level of complexity as found in biological organisms.… (More)

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