Difference between revisions of "Evolutionary Computation / Genetic Algorithms"
m |
m |
||
| (3 intermediate revisions by the same user not shown) | |||
| Line 9: | Line 9: | ||
* [[Symbiotic Intelligence]] ... [[Bio-inspired Computing]] ... [[Neuroscience]] ... [[Connecting Brains]] ... [[Nanobots#Brain Interface using AI and Nanobots|Nanobots]] ... [[Molecular Artificial Intelligence (AI)|Molecular]] ... [[Neuromorphic Computing|Neuromorphic]] ... [[Evolutionary Computation / Genetic Algorithms| Evolutionary/Genetic]] | * [[Symbiotic Intelligence]] ... [[Bio-inspired Computing]] ... [[Neuroscience]] ... [[Connecting Brains]] ... [[Nanobots#Brain Interface using AI and Nanobots|Nanobots]] ... [[Molecular Artificial Intelligence (AI)|Molecular]] ... [[Neuromorphic Computing|Neuromorphic]] ... [[Evolutionary Computation / Genetic Algorithms| Evolutionary/Genetic]] | ||
| + | * [[NeuroEvolution of Augmenting Topologies (NEAT)]] | ||
* [[Reinforcement Learning (RL)]] | * [[Reinforcement Learning (RL)]] | ||
** [[Monte Carlo]] (MC) Method - Model Free Reinforcement Learning | ** [[Monte Carlo]] (MC) Method - Model Free Reinforcement Learning | ||
| Line 24: | Line 25: | ||
* [[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)] | ||
| Line 30: | Line 33: | ||
* [[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] | ||
| − | |||
| − | |||
* [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] | ||
| Line 37: | Line 38: | ||
<youtube>HT1_BHA3ecY</youtube> | <youtube>HT1_BHA3ecY</youtube> | ||
| − | |||
<youtube>rGWBo0JGf50</youtube> | <youtube>rGWBo0JGf50</youtube> | ||
<youtube>dSofAXnnFrY</youtube> | <youtube>dSofAXnnFrY</youtube> | ||
| Line 59: | Line 59: | ||
<youtube>ooA0J6DWWTM</youtube> | <youtube>ooA0J6DWWTM</youtube> | ||
<youtube>Xx0SsffdMBw</youtube> | <youtube>Xx0SsffdMBw</youtube> | ||
| − | |||
<youtube>ncG_mn9BtRA</youtube> | <youtube>ncG_mn9BtRA</youtube> | ||
Latest revision as of 16:20, 25 February 2024
Youtube search... ...Google search
- Symbiotic Intelligence ... Bio-inspired Computing ... Neuroscience ... Connecting Brains ... Nanobots ... Molecular ... Neuromorphic ... Evolutionary/Genetic
- NeuroEvolution of Augmenting Topologies (NEAT)
- 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
