Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
A common genetic encoding for both direct and indirect encodings of networks
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolving a dynamic predictive coding mechanism for novelty detection
Knowledge-Based Systems
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Accelerating neuroevolutionary methods using a Kalman filter
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Accelerated Neural Evolution through Cooperatively Coevolved Synapses
The Journal of Machine Learning Research
Analysis of an evolutionary reinforcement learning method in a multiagent domain
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
The 2007 IEEE CEC simulated car racing competition
Genetic Programming and Evolvable Machines
A General Framework for Encoding and Evolving Neural Networks
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Evolving Neural Networks for Online Reinforcement Learning
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
KI '08 Proceedings of the 31st annual German conference on Advances in Artificial Intelligence
Using Gaussian Processes to Optimize Expensive Functions
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Evolving novelty detectors for specific applications
Neurocomputing
Learning Dynamic Obstacle Avoidance for a Robot Arm Using Neuroevolution
Neural Processing Letters
On the significance of the permutation problem in neuroevolution
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
NEAT in increasingly non-linear control situations
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
A call admission control scheme using neuroevolution algorithm in cellular networks
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Global shape with morphogen gradients and motile polarized cells
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolved Controllers for Simulated Locomotion
MIG '09 Proceedings of the 2nd International Workshop on Motion in Games
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Stepwise transition from direct encoding to artificial ontogeny in neuroevolution
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
A NEAT Way for Evolving Echo State Networks
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
EA'09 Proceedings of the 9th international conference on Artificial evolution
Applied Computational Intelligence and Soft Computing - Special issue on theory and applications of evolutionary computation
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
Learning n-tuple networks for othello by coevolutionary gradient search
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Neuroevolution with analog genetic encoding
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Artificial Intelligence Review
Distance measures for HyperGP with fitness sharing
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Introducing novelty search in evolutionary swarm robotics
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
Adaptive reservoir computing through evolution and learning
Neurocomputing
Neuroannealing: martingale optimization for neural networks
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Advanced overtaking behaviors for blocking opponents in racing games using a fuzzy architecture
Expert Systems with Applications: An International Journal
Solving the pole balancing problem by means of assembler encoding
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Mimicking human neuronal pathways in silico: an emergent model on the effective connectivity
Journal of Computational Neuroscience
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Artificial neural networks can potentially control autonomous robots, vehicles, factories, or game players more robustly than traditional approaches. Neuroevolution, i.e. the artificial evolution of neural networks, is a method for finding the right topology and connection weights to specify the desired control behavior. The challenge for neuroevolution is that difficult tasks may require complex networks with many connections, all of which must be set to the right values. Even if a network exists that can solve the task, evolution may not be able to find it in such a high-dimensional search space. This dissertation presents the NeuroEvolution of Augmenting Topologies (NEAT) method, which makes search for complex solutions feasible. In a process called complexification, NEAT begins by searching in a space of simple networks, and gradually makes them more complex as the search progresses. By starting minimally, NEAT is more likely to find efficient and robust solutions than neuroevolution methods that begin with large fixed or randomized topologies; by elaborating on existing solutions, it can gradually construct even highly complex solutions. In this dissertation, NEAT is first shown faster than traditional approaches on a challenging reinforcement learning benchmark task. Second, by building on existing structure, it is shown to maintain an “arms race” even in open-ended coevolution. Third, NEAT is used to successfully discover complex behavior in three challenging domains: the game of Go, an automobile warning system, and a real-time interactive video game. Experimental results in these domains demonstrate that NEAT makes entirely new applications of machine learning possible.