Evolving Neural Networks through Augmenting Topologies
@article{Stanley2002EvolvingNN, title={Evolving Neural Networks through Augmenting Topologies}, author={Kenneth O. Stanley and Risto Miikkulainen}, journal={Evolutionary Computation}, year={2002}, volume={10}, pages={99-127} }
An important question in neuroevolution is how to gain an advantage from evolving neural network topologies along with weights. [] Key Result NEAT is also an important contribution to GAs because it shows how it is possible for evolution to both optimize and complexify solutions simultaneously, offering the possibility of evolving increasingly complex solutions over generations, and strengthening the analogy with biological evolution.
3,149 Citations
Efficient evolution of neural network topologies
- Biology, Computer ScienceProceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)
- 2002
A method, NeuroEvolution of Augmenting Topologies (NEAT) that outperforms the best fixed-topology methods on a challenging benchmark reinforcement learning task and shows how it is possible for evolution to both optimize and complexify solutions simultaneously, making it possible to evolve increasingly complex solutions over time.
Advances in Neuroevolution through Augmenting Topologies – A Case Study
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An analysis of the efficiency and performance of the various algorithms which have been proposed for Topology and Weight Evolving Artificial Neural Networks (TWEANNs) will provide learners with a better overview of the past and current research trends in the field of Neuroevolution.
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A visual and statistical analysis contrasting the behaviour of NEAT, with and without using the crossover operator, when solving the two benchmark problems outlined in the original NEAT article: XOR and double-pole balancing.
Competitive Coevolution through Evolutionary Complexification
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It is argued that complexification, i.e. the incremental elaboration of solutions through adding new structure, achieves both these goals and is demonstrated through the NeuroEvolution of Augmenting Topologies (NEAT) method, which evolves increasingly complex neural network architectures.
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A novel neurocoevolutionary algorithm, EEC, is proposed in this work, where the connection weights and the connection paths of networks are evolved separately and demonstrates that fully connected networks could generate noise which results in inefficient learning.
Neuroevolution through Augmenting Topologies Applied to Evolving Neural Networks to Play Othello
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A powerful new algorithm for neuroevolution, Neuro-Evolution for Augmenting Topologies (NEAT), is adapted to the game playing domain and illustrated the necessity of the mobility strategy in defeating a powerful positional player in Othello.
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A new network called Blocky Net with built-in feature selection, and a limited maximum parameter space and complexity is proposed with better performance on 13 of the 20 datasets tested versus 2 for FS-NEAT, and is better than NEAT in all cases.
Meta-NEAT, meta-analysis of neuroevolving topologies
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Meta-NEAT offers a way to optimize the convergence rate of NEAT through the use of an additional genetic algorithm built on top ofNEAT which adds an additional layer which learns optimal hyper-parameter configurations in order to boost the convergence rates of NEat.
Automatic Task Decomposition for the NeuroEvolution of Augmenting Topologies (NEAT) Algorithm
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An algorithm for evolving MFFN architectures based on the NeuroEvolution of Augmenting Topologies (NEAT) algorithm is presented, outlining an approach to automatically evolving, attributing fitness values and combining the task specific networks in a principled manner.
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This paper advances a method which incorporates a type of topological edge coding, named Reverse Encoding Tree (RET), for evolving scalable neural networks efficiently, and demonstrates that RET expends potential future research directions in dynamic environments.
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