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Evolving Neural Networks through Augmenting Topologies

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Description:MIT Press Journal Article on Neural NetworksAbstractAn important question in neuroevolution is how to gain an advantage from evolvingneural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topologymethod on a challenging benchmark reinforcement learning task. We claim that theincreased 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 ablationstudies that demonstrate that each component is necessary to the system as a wholeand 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 bothoptimize and complexify solutions simultaneously, offering the possibility of evolvingincreasingly complex solutions over generations, and strengthening the analogy withbiological evolution.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Evolving Neural Networks through Augmenting Topologies. To get started finding Evolving Neural Networks through Augmenting Topologies, you are right to find our website which has a comprehensive collection of manuals listed.
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Publisher: MIT Press Journal

Release: 2002

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Description: MIT Press Journal Article on Neural NetworksAbstractAn important question in neuroevolution is how to gain an advantage from evolvingneural network topologies along with weights. We present a method, NeuroEvolution of Augmenting Topologies (NEAT), which outperforms the best fixed-topologymethod on a challenging benchmark reinforcement learning task. We claim that theincreased 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 ablationstudies that demonstrate that each component is necessary to the system as a wholeand 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 bothoptimize and complexify solutions simultaneously, offering the possibility of evolvingincreasingly complex solutions over generations, and strengthening the analogy withbiological evolution.We have made it easy for you to find a PDF Ebooks without any digging. And by having access to our ebooks online or by storing it on your computer, you have convenient answers with Evolving Neural Networks through Augmenting Topologies. To get started finding Evolving Neural Networks through Augmenting Topologies, you are right to find our website which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of different products represented.

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Evolving Neural Networks through Augmenting Topologies

Evolving Neural Networks through Augmenting Topologies

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