Difference between revisions of "Topology and Weight Evolving Artificial Neural Network (TWEANN)"
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| − | [http://www.youtube.com/results?search_query=TWEANN | + | [http://www.youtube.com/results?search_query=TWEANN+neural+artificial+intelligence Youtube search...] |
| − | [http://www.google.com/search?q=TWEANN | + | [http://www.google.com/search?q=TWEANN+machine+learning+ML ...Google search] |
* [[Architectures]] | * [[Architectures]] | ||
Revision as of 21:35, 19 January 2019
Youtube search... ...Google search
- Architectures
- NeuroEvolution | Wikipedia
- Evolutionary Computation / Genetic Algorithms
- Gradient Descent Optimization & Challenges
- NeuroEvolution of Augmenting Topologies (NEAT)
Many neuroevolution algorithms have been defined. One common distinction is between algorithms that evolve only the strength of the connection weights for a fixed network topology (sometimes called conventional neuroevolution), as opposed to those that evolve both the topology of the network and its weights
Comparison with gradient descent
Most neural networks use gradient descent rather than neuroevolution. However, around 2017 researchers at Uber stated they had found that simple structural neuroevolution algorithms were competitive with sophisticated modern industry-standard gradient-descent deep learning algorithms, in part because neuroevolution was found to be less likely to get stuck in local minima.
