In these tutorials, we will demonstrate and visualize algorithms like Genetic Algorithm, Evolution Strategy, NEAT etc. All methods mentioned below have their video and text tutorial in Chinese. Visit 莫烦 Python for more.
evolutionary-algorithm genetic-algorithm neuroevolution microbial-genetic-algorithm travel-sale-problem evolution-strategy es reinforcement-learning neural-network microbial-ga neat neural-nets travel-sales-problem nes evolution-strategies openai distributed-es machine-learning tutorialCURRENTLY NOT WORKING! There will be a further notice when it's updated.NEAT (NeuroEvolution of Augmenting Topologies) is a neuroevolution algorithm by Dr. Kenneth O. Stanley which evolves not only neural networks' weights but also their topologies. This method starts the evolution process with genomes with minimal structure, then complexifies the structure of each genome as it progresses. You can read the original paper from here.
neat neuroevolution neural-network topologies reinforcement-learning recurrent-neural-networks genetic-algorithm machine-learning go-libraryThis implementations uses a Continuous-Time Recurrent Neural Network (CTRNN) (Yamauchi and Beer, 1994).
neuroevolution augmenting-topologies-neatThis repository provides implementation of NeuroEvolution of Augmenting Topologies (NEAT) method written in Go language. The Neuroevolution (NE) is an artificial evolution of Neural Networks (NN) using genetic algorithms in order to find optimal NN parameters and topology. Neuroevolution of NN may assume search for optimal weights of connections between NN nodes as well as search for optimal topology of resulting NN. The NEAT method implemented in this work do search for both: optimal connections weights and topology for given task (number of NN nodes per layer and their interconnections).
artificial-neural-networks neuroevolution neat augmenting-topologies unsupervised-learning unsupervised-machine-learning neural-network reinforcement-learning-algorithms reinforcement-learningThis repository provides implementation of Neuro-Evolution of Augmented Topologies (NEAT) with Novelty Search optimization implemented in GoLang. The Neuro-Evolution (NE) is an artificial evolution of Neural Networks (NN) using genetic algorithms in order to find optimal NN parameters and topology. Neuro-Evolution of NN may assume search for optimal weights of connections between NN nodes as well as search for optimal topology of resulting NN. The NEAT method implemented in this work do search for both: optimal connections weights and topology for given task (number of NN nodes per layer and their interconnections).
neuroevolution neat novelty-search artificial-neural-networks augmenting-topologies unsupervised-learning unsupervised-machine-learning unsupervised-learning-algorithms reinforcement-learning-algorithms modular-ai explainable-ai explainable-artificial-intelligenceCurrently this codebase only works with python 2. The following libraries are needed: keras, numpy, and sklearn.
denser neuroevolution evolutionary-computation machine-learning deep-learning convolutional-neural-networksIf you would like to fund future work then donations are welcome via Open Collective or Patreon. NEAT is NeuroEvolution of Augmenting Topologies; an evolutionary algorithm devised by Kenneth O. Stanley.
ai evolutionary-computation reinforcement-learning neural-network neat sharpneat evolution evolutionary-algorithm neuroevolution neural-networks
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