Difference between revisions of "Evolutionary Computation / Genetic Algorithms"
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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
}} | }} | ||
| − | [ | + | [https://www.youtube.com/results?search_query=evolution+genetic+algorithm+in+neural+artificial+intelligence Youtube search...] |
| − | [ | + | [https://www.google.com/search?q=evolution+genetic+algorithm+machine+learning+ML ...Google search] |
| + | * [[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 16: | Line 18: | ||
** [[Deep Reinforcement Learning (DRL)]] DeepRL | ** [[Deep Reinforcement Learning (DRL)]] DeepRL | ||
** [[Distributed Deep Reinforcement Learning (DDRL)]] | ** [[Distributed Deep Reinforcement Learning (DDRL)]] | ||
| − | |||
** [[Actor Critic]] | ** [[Actor Critic]] | ||
*** [[Asynchronous Advantage Actor Critic (A3C)]] | *** [[Asynchronous Advantage Actor Critic (A3C)]] | ||
| Line 22: | Line 23: | ||
*** [[Lifelong Latent Actor-Critic (LILAC)]] | *** [[Lifelong Latent Actor-Critic (LILAC)]] | ||
** [[Hierarchical Reinforcement Learning (HRL)]] | ** [[Hierarchical Reinforcement Learning (HRL)]] | ||
| − | |||
| − | |||
* [[Architectures]] | * [[Architectures]] | ||
| − | * [[Python#TPOT|TPOT]] - automates the building of ML [[AIOps | | + | * [[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://sig.sigevo.org ACM Special Interest Group on Genetic and Evolutionary Computation (SIGEVO)] | ||
| + | * [https://www.evolvingai.org/publications Publication - Evolving Artificial Intelligence Laboratory | University of Wyoming] | ||
* [[NeuroEvolution of Augmenting Topologies (NEAT)]] | * [[NeuroEvolution of Augmenting Topologies (NEAT)]] | ||
* [[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://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] | |
| − | |||
| − | * [ | ||
<youtube>HT1_BHA3ecY</youtube> | <youtube>HT1_BHA3ecY</youtube> | ||
| − | |||
<youtube>rGWBo0JGf50</youtube> | <youtube>rGWBo0JGf50</youtube> | ||
<youtube>dSofAXnnFrY</youtube> | <youtube>dSofAXnnFrY</youtube> | ||
| Line 57: | Line 55: | ||
== Nature == | == Nature == | ||
| − | [ | + | [https://www.youtube.com/results?search_query=biology+life+evolution+artificial+intelligence Youtube search...] |
<youtube>ooA0J6DWWTM</youtube> | <youtube>ooA0J6DWWTM</youtube> | ||
<youtube>Xx0SsffdMBw</youtube> | <youtube>Xx0SsffdMBw</youtube> | ||
| − | |||
<youtube>ncG_mn9BtRA</youtube> | <youtube>ncG_mn9BtRA</youtube> | ||
== Evolution of Mind == | == Evolution of Mind == | ||
| − | [ | + | [https://www.youtube.com/results?search_query=information+mind+unified+evolution Youtube search...] |
<youtube>AZX6awZq5Z0</youtube> | <youtube>AZX6awZq5Z0</youtube> | ||
<youtube>IZefk4gzQt4</youtube> | <youtube>IZefk4gzQt4</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
