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- Neuroevolution_of_augmenting_topologies abstract "NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm for the generation of evolving artificial neural networks (a neuroevolution technique) developed by Ken Stanley in 2002 while at The University of Texas at Austin. It alters both the weighting parameters and structures of networks, attempting to find a balance between the fitness of evolved solutions and their diversity. It is based on applying three key techniques: tracking genes with history markers to allow crossover among topologies, applying speciation (the evolution of species) to preserve innovations, and developing topologies incrementally from simple initial structures ("complexifying").".
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- Neuroevolution_of_augmenting_topologies wikiPageExternalLink stanley.cec03.pdf.
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- Neuroevolution_of_augmenting_topologies subject Category:Evolutionary_algorithms.
- Neuroevolution_of_augmenting_topologies subject Category:Evolutionary_computation.
- Neuroevolution_of_augmenting_topologies subject Category:Genetic_algorithms.
- Neuroevolution_of_augmenting_topologies subject Category:Neural_networks.
- Neuroevolution_of_augmenting_topologies type Abstraction100002137.
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- Neuroevolution_of_augmenting_topologies type NeuralNetwork106725467.
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- Neuroevolution_of_augmenting_topologies comment "NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm for the generation of evolving artificial neural networks (a neuroevolution technique) developed by Ken Stanley in 2002 while at The University of Texas at Austin. It alters both the weighting parameters and structures of networks, attempting to find a balance between the fitness of evolved solutions and their diversity.".
- Neuroevolution_of_augmenting_topologies label "NeuroEvolution of Augmented Topologies".
- Neuroevolution_of_augmenting_topologies label "Neuroevolution of augmenting topologies".
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