Difference between revisions of "Evolutionary Computation / Genetic Algorithms"
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* [[Architectures]] | * [[Architectures]] | ||
* [[Python#TPOT|TPOT]] - automates the building of ML [[Algorithm Administration#AIOps/MLOps|pipelines]] by combining a flexible expression tree representation of [[Algorithm Administration#AIOps/MLOps|pipelines]] with stochastic search algorithms such as genetic programming. | * [[Python#TPOT|TPOT]] - automates the building of ML [[Algorithm Administration#AIOps/MLOps|pipelines]] by combining a flexible expression tree representation of [[Algorithm Administration#AIOps/MLOps|pipelines]] with stochastic search algorithms such as genetic programming. | ||
| + | * [[Other Challenges]] in Artificial Intelligence | ||
| + | * [[Reinforcement Learning (RL)]] | ||
* [https://en.wikipedia.org/wiki/Neural_architecture_search#NAS_with_Evolution Neural Architecture Search (NAS) with Evolution | Wikipedia] | * [https://en.wikipedia.org/wiki/Neural_architecture_search#NAS_with_Evolution Neural Architecture Search (NAS) with Evolution | Wikipedia] | ||
* [https://sig.sigevo.org ACM Special Interest Group on Genetic and Evolutionary Computation (SIGEVO)] | * [https://sig.sigevo.org ACM Special Interest Group on Genetic and Evolutionary Computation (SIGEVO)] | ||
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* [[Topology and Weight Evolving Artificial Neural Network (TWEANN)]] | * [[Topology and Weight Evolving Artificial Neural Network (TWEANN)]] | ||
* [https://medium.com/@moocaholic/2017-the-year-of-neuroevolution-30e59ae8fe18 2017: The Year of Neuroevolution | Grigory Sapunov] | * [https://medium.com/@moocaholic/2017-the-year-of-neuroevolution-30e59ae8fe18 2017: The Year of Neuroevolution | Grigory Sapunov] | ||
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* [https://pathmind.com/wiki/evolutionary-genetic-algorithm A Beginner's Guide to Genetic & Evolutionary Algorithms | Chris Nicholson - A.I. Wiki pathmind] | * [https://pathmind.com/wiki/evolutionary-genetic-algorithm A Beginner's Guide to Genetic & Evolutionary Algorithms | Chris Nicholson - A.I. Wiki pathmind] | ||
* [https://bookdown.org/max/FES/genetic-algorithms.html Feature Engineering and Selection: A Practical Approach for Predictive Models -12.3 Genetic Algorithms | Max Kuhn and Kjell Johnson] | * [https://bookdown.org/max/FES/genetic-algorithms.html Feature Engineering and Selection: A Practical Approach for Predictive Models -12.3 Genetic Algorithms | Max Kuhn and Kjell Johnson] | ||
Revision as of 09:46, 6 August 2023
Youtube search... ...Google search
- Symbiotic Intelligence ... Bio-inspired Computing ... Neuroscience ... Connecting Brains ... Nanobots ... Molecular ... Neuromorphic ... Evolutionary/Genetic
- Reinforcement Learning (RL)
- Monte Carlo (MC) Method - Model Free Reinforcement Learning
- Markov Decision Process (MDP)
- State-Action-Reward-State-Action (SARSA)
- Q Learning
- Deep Reinforcement Learning (DRL) DeepRL
- Distributed Deep Reinforcement Learning (DDRL)
- Actor Critic
- Hierarchical Reinforcement Learning (HRL)
- Architectures
- TPOT - automates the building of ML pipelines by combining a flexible expression tree representation of pipelines with stochastic search algorithms such as genetic programming.
- Other Challenges in Artificial Intelligence
- Reinforcement Learning (RL)
- Neural Architecture Search (NAS) with Evolution | Wikipedia
- ACM Special Interest Group on Genetic and Evolutionary Computation (SIGEVO)
- Publication - Evolving Artificial Intelligence Laboratory | University of Wyoming
- NeuroEvolution of Augmenting Topologies (NEAT)
- Topology and Weight Evolving Artificial Neural Network (TWEANN)
- 2017: The Year of Neuroevolution | Grigory Sapunov
- A Beginner's Guide to Genetic & Evolutionary Algorithms | Chris Nicholson - A.I. Wiki pathmind
- Feature Engineering and Selection: A Practical Approach for Predictive Models -12.3 Genetic Algorithms | Max Kuhn and Kjell Johnson
Nature
Evolution of Mind
