Engineering industry controllers using neuroevolution
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Coevolution of neural networks using a layered pareto archive
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Dynamic High Frequency Trading: A Neuro-Evolutionary Approach
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Evolving novelty detectors for specific applications
Neurocomputing
Learning Human-Level AI abilities to drive racing cars
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
NEAT in increasingly non-linear control situations
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
Evolution of recollection and prediction in neural networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Neuroevolution, i.e. evolving artificial neural networks with genetic algorithms, has been highly effective in reinforcement learning tasks, particularly those with hidden state information. 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) that outperforms the best fixed-topology methods on a challenging benchmark reinforcement learning task. We claim that the increased efficiency is due to (1) employing a principled method of crossover of different topologies, (2) protecting structural innovation using speciation, and (3) incrementally growing from minimal structure. We test this claim through a series of ablation studies that demonstrate that each component is necessary to the system as a whole and to each other. What results is significantly faster learning. NEAT is also an important contribution to GAs because it shows how it is possible for evolution to both optimize and complexify solutions simultaneously, making it possible to evolve increasingly complex solutions over time, thereby strengthening the analogy with biological evolution.