HyperNEAT Controlled Robots Learn How to Drive on Roads in Simulated Environment ˇ Jan Drchal, Jan Koutn´ık, Miroslav Snorek Abstract— In this paper we describe simulation of autonomous robots controlled by recurre

Source URL: cig.felk.cvut.cz

The Sensitivity of HyperNEAT to Different Geometric Representations of a Problem Jeff Clune Charles Ofria

Source URL: www.ofria.com

Evolving Robot Gaits in Hardware: the HyperNEAT Generative Encoding Vs. Parameter Optimization Jason Yosinski1 , Jeff Clune1 , Diana Hidalgo1 , Sarah Nguyen1 , Juan Cristobal Zagal2 , and Hod Lipson1 1 2

Source URL: yosinski.com

Constraining Connectivity to Encourage Modularity in HyperNEAT In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCONew York, NY: ACM, 2011 Phillip Verbancsics

Source URL: eplex.cs.ucf.edu

• Science
• Cybernetics
• Evolutionary computation
• Genetic algorithms
• Computational neuroscience
• Neuroevolution
• HyperNEAT
• Compositional pattern-producing network
• Artificial neural network
• Neural networks
• Evolutionary algorithms
• Machine learning

A STEP TOWARD EVOLVING BIPED WALKING BEHAVIOR THROUGH INDIRECT ENCODING by RANDAL S. OLSON

Source URL: www.randalolson.com

• Neural networks
• Animal anatomy
• Biology
• Zoology
• Evolutionary computation
• Neuroevolution
• Bipedalism
• HyperNEAT
• Compositional pattern-producing network
• Locomotion
• Evolutionary algorithms
• Motion

Evolving Policy Geometry for Scalable Multiagent Learning In: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS[removed]David B. D’Ambrosio, Joel Lehman, Sebastian Risi, a

Source URL: eplex.cs.ucf.edu

• Artificial intelligence
• Science
• Evolutionary computation
• Genetic algorithms
• Multi-agent systems
• HyperNEAT
• Agent-based model
• Compositional pattern-producing network
• Neuroevolution
• Neural networks
• Machine learning
• Evolutionary algorithms

Evolving the Placement and Density of Neurons in the HyperNEAT Substrate In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO[removed]New York, NY: ACM Winner of the Best Paper Award in the Genera

Source URL: eplex.cs.ucf.edu

• Cybernetics
• Computational neuroscience
• Evolutionary computation
• Genetic algorithms
• Artificial intelligence
• Neuroevolution
• HyperNEAT
• Artificial neural network
• Compositional pattern-producing network
• Neural networks
• Evolutionary algorithms
• Machine learning

Indirectly Encoding Neural Plasticity as a Pattern of Local Rules In: Proceedings of the 11th International Conference on Simulation of Adaptive Behavior (SAB[removed]New York, NY: Springer Sebastian Risi and Kenneth O.

Source URL: eplex.cs.ucf.edu

• Cybernetics
• Machine learning
• Science
• Evolutionary computation
• Neuroscience
• Neuroevolution
• Artificial neural network
• HyperNEAT
• Compositional pattern-producing network
• Neural networks
• Evolutionary algorithms
• Computational neuroscience

Citation: Clune J, Beckmann BE, Pennock RT, and Ofria C[removed]HybrID: A hybridization of indirect and direct encodings for evolutionary computation. Proceedings of the European Conference on Artificial Life. Pages 1-8.

Source URL: ofria.com

• Applied mathematics
• Machine learning
• Artificial intelligence
• Evolutionary computation
• Cybernetics
• Neuroevolution
• HyperNEAT
• Compositional pattern-producing network
• Artificial neural network
• Evolutionary algorithms
• Neural networks
• Genetic algorithms

How a Generative Encoding Fares as Problem-Regularity Decreases Jeff Clune1, Charles Ofria1, and Robert T. Pennock1,2 Department of Computer Science and Engineering, Department of Philosophy & Lyman Briggs College Michig

Source URL: ofria.com

• Genetic algorithms
• Machine learning
• Applied mathematics
• Artificial intelligence
• Neuroevolution
• HyperNEAT
• Computational neuroscience
• Compositional pattern-producing network
• Neural networks
• Evolutionary algorithms
• Evolutionary computation