D-Index & Metrics Best Publications

Kenneth O. Stanley

University of Central Florida
United States

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name Citations Publications World Ranking National Ranking

Computer Science D-index 64 Citations 19,281 216 World Ranking 1607 National Ranking 890

Overview

What is he best known for?

The fields of study he is best known for:

Artificial intelligence
Machine learning
Artificial neural network

Kenneth O. Stanley mostly deals with Artificial intelligence, Artificial neural network, Neuroevolution, Neuroevolution of augmenting topologies and Evolutionary computation. His work on Reinforcement learning and Evolutionary algorithm as part of general Artificial intelligence research is often related to Novelty, thus linking different fields of science. His Artificial neural network study combines topics from a wide range of disciplines, such as Robot and Deep learning.

Much of his study explores Neuroevolution relationship to Video game design. His Neuroevolution of augmenting topologies research integrates issues from Online community, HyperNEAT and Multimedia. His studies deal with areas such as Evolving networks, Time delay neural network and Search algorithm as well as Evolutionary computation.

His most cited work include:

Evolving neural networks through augmenting topologies (2279 citations)
Abandoning objectives: Evolution through the search for novelty alone (586 citations)
A hypercube-based encoding for evolving large-scale neural networks (557 citations)

What are the main themes of his work throughout his whole career to date?

His main research concerns Artificial intelligence, Artificial neural network, Neuroevolution, Machine learning and Evolutionary computation. His HyperNEAT, Reinforcement learning, Evolutionary algorithm and Encoding study in the realm of Artificial intelligence connects with subjects such as Novelty. His research investigates the connection between Reinforcement learning and topics such as Backpropagation that intersect with issues in Stochastic gradient descent.

His research in Artificial neural network intersects with topics in Domain, Robot and Deep learning. His biological study spans a wide range of topics, including Interactive evolution, Human–computer interaction and Benchmark. His research investigates the connection between Neuroevolution of augmenting topologies and topics such as Video game that intersect with problems in Video game design.

He most often published in these fields:

Artificial intelligence (63.68%)
Artificial neural network (41.45%)
Neuroevolution (23.93%)

What were the highlights of his more recent work (between 2016-2021)?

Artificial intelligence (63.68%)
Reinforcement learning (17.52%)
Artificial neural network (41.45%)

In recent papers he was focusing on the following fields of study:

His primary areas of study are Artificial intelligence, Reinforcement learning, Artificial neural network, Neuroevolution and Artificial life. His Artificial intelligence study frequently draws connections between related disciplines such as Machine learning. His Reinforcement learning research includes themes of Gradient descent, Genetic algorithm, Local optimum and Human–computer interaction.

Kenneth O. Stanley has included themes like Variety and Deep learning in his Artificial neural network study. His research investigates the connection with Neuroevolution and areas like Evolutionary robotics which intersect with concerns in Sandbox, Video game and Anticipation. His Artificial life study incorporates themes from Domain, Evolutionary computation and Industrial engineering.

Between 2016 and 2021, his most popular works were:

Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning (333 citations)
Designing neural networks through neuroevolution (201 citations)
Designing neural networks through neuroevolution (201 citations)

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Evolving neural networks through augmenting topologies

Kenneth O. Stanley;Risto Miikkulainen.
Evolutionary Computation (2002)

3710 Citations

Abandoning objectives: Evolution through the search for novelty alone

Joel Lehman;Kenneth O. Stanley.
Evolutionary Computation (2011)

878 Citations

A hypercube-based encoding for evolving large-scale neural networks

Kenneth O. Stanley;David B. D'Ambrosio;Jason Gauci.
Artificial Life (2009)

874 Citations

Search-Based Procedural Content Generation: A Taxonomy and Survey

J. Togelius;G. N. Yannakakis;K. O. Stanley;C. Browne.
IEEE Transactions on Computational Intelligence and AI in Games (2011)

771 Citations

Compositional pattern producing networks: A novel abstraction of development

Kenneth O. Stanley.
Genetic Programming and Evolvable Machines (2007)

725 Citations

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

Felipe Petroski Such;Vashisht Madhavan;Edoardo Conti;Joel Lehman.
arXiv: Neural and Evolutionary Computing (2017)

618 Citations

A Taxonomy for artificial embryogeny

Kenneth O. Stanley;Risto Miikkulainen.
Artificial Life (2003)

583 Citations

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

Joel Lehman;Kenneth O. Stanley.
Artificial Life (2008)

537 Citations

Competitive coevolution through evolutionary complexification

Kenneth O. Stanley;Risto Miikkulainen.
Journal of Artificial Intelligence Research (2004)

537 Citations

Real-time neuroevolution in the NERO video game

K.O. Stanley;B.D. Bryant;R. Miikkulainen.
IEEE Transactions on Evolutionary Computation (2005)

506 Citations

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