NeuroEvolution of Augmenting Topologies (NEAT) - Revision history

BPeat at 17:07, 6 August 2023

2023-08-06T17:07:02Z

BPeat at 17:06, 6 August 2023

2023-08-06T17:06:28Z

BPeat at 17:03, 6 August 2023

2023-08-06T17:03:13Z

BPeat at 17:02, 6 August 2023

2023-08-06T17:02:31Z

BPeat at 16:59, 6 August 2023

2023-08-06T16:59:56Z

BPeat at 03:52, 3 February 2019

2019-02-03T03:52:08Z

BPeat at 01:58, 20 January 2019

2019-01-20T01:58:01Z

BPeat: Created page with "[http://www.youtube.com/results?search_query=NeuroEvolution+Augmenting+Topologies+NEAT+neural+artificial+intelligence Youtube search...] [http://www.google.com/search?q=NeuroE..."

2019-01-20T01:41:38Z

Created page with "[http://www.youtube.com/results?search_query=NeuroEvolution+Augmenting+Topologies+NEAT+neural+artificial+intelligence Youtube search...] [http://www.google.com/search?q=NeuroE..."

New page

[http://www.youtube.com/results?search_query=NeuroEvolution+Augmenting+Topologies+NEAT+neural+artificial+intelligence Youtube search...]
[http://www.google.com/search?q=NeuroEvolution+Augmenting+Topologies+NEAT+machine+learning+ML ...Google search]

* [[Architectures]]
* [http://en.wikipedia.org/wiki/Neuroevolution NeuroEvolution | Wikipedia]
* [[Evolutionary Computation / Genetic Algorithms]]
* [[Gradient Descent Optimization & Challenges]]
* [[Topology and Weight Evolving Artificial Neural Network (TWEANN)]]

http://cdn-images-1.medium.com/max/600/1*SrhRHdHjTDkAbq8W-ZMfSQ.png

<youtube>kHyNqSnzP8Y</youtube>

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 #<b>Initialization</b>: NEAT starts with a population of simple neural networks, often with a minimal structure.
 #<b>Genetic Operators</b>: It uses genetic operations such as mutation and crossover to create new networks from the existing population. Mutation involves making small changes to the structure and weights of neural networks, while crossover combines the structures of two parent networks to create offspring.
 #<b>Historical Markings</b>: NEAT introduces historical markings to keep track of structural innovations that occurred throughout generations. This allows the algorithm to maintain innovation over time and avoid the loss of beneficial structures during the evolutionary process.
 #<b>Speciation</b>: To encourage diversity and prevent premature convergence, NEAT groups individuals into species based on their structural similarity. Each species is independently evolved, promoting exploration in different regions of the search space.
 #<b>Fitness Evaluation</b>: The fitness of each neural network is evaluated based on its performance on the given task. Networks with higher fitness scores have a better chance of being selected for the next generation.
 #<b>Reproduction</b>: NEAT uses a generational approach, where individuals with higher fitness scores are selected to produce offspring for the next generation. The offspring inherit the structures and weights from their parents.

← Older revision		Revision as of 17:06, 6 August 2023
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	* [http://en.wikipedia.org/wiki/Neuroevolution NeuroEvolution \| Wikipedia]		* [http://en.wikipedia.org/wiki/Neuroevolution NeuroEvolution \| Wikipedia]
	* [[Evolutionary Computation / Genetic Algorithms]]		* [[Evolutionary Computation / Genetic Algorithms]]
		+
		+
		+	NeuroEvolution of Augmenting Topologies (NEAT) is a powerful evolutionary algorithm designed for evolving [[Neural Network\|artificial neural networks (ANNs)]]. NEAT is a significant contribution to the field of neuroevolution and has inspired various extensions and improvements in the evolutionary computation community. It remains a popular and influential algorithm in the realm of artificial intelligence and machine learning.It was introduced by Kenneth O. Stanley and Risto Miikkulainen in 2002. NEAT combines ideas from both genetic algorithms and neural networks to optimize the structure and weights of neural networks for various tasks. The key feature of NEAT is its ability to evolve neural network architectures over generations. Unlike traditional neural network training methods that use fixed architectures, NEAT starts with small, simple networks and allows them to grow in complexity and size through generations. It achieves this by introducing innovations such as historical markings and speciation.
		+
		+	The NEAT algorithm operates as follows:
		+
		+	#<b>Initialization</b>: NEAT starts with a population of simple neural networks, often with a minimal structure.
		+
		+	#<b>Genetic Operators</b>: It uses genetic operations such as mutation and crossover to create new networks from the existing population. Mutation involves making small changes to the structure and weights of neural networks, while crossover combines the structures of two parent networks to create offspring.
		+
		+	#<b>Historical Markings</b>: NEAT introduces historical markings to keep track of structural innovations that occurred throughout generations. This allows the algorithm to maintain innovation over time and avoid the loss of beneficial structures during the evolutionary process.
		+
		+	#<b>Speciation</b>: To encourage diversity and prevent premature convergence, NEAT groups individuals into species based on their structural similarity. Each species is independently evolved, promoting exploration in different regions of the search space.
		+
		+	#<b>Fitness Evaluation</b>: The fitness of each neural network is evaluated based on its performance on the given task. Networks with higher fitness scores have a better chance of being selected for the next generation.
		+
		+	#<b>Reproduction</b>: NEAT uses a generational approach, where individuals with higher fitness scores are selected to produce offspring for the next generation. The offspring inherit the structures and weights from their parents.
		+
		+	Through these iterative steps, NEAT evolves neural networks that are well-suited for the given task. It has been successfully applied to various problems, including game playing, control tasks, and pattern recognition. NEAT's ability to discover novel and efficient network architectures without human intervention makes it particularly useful in scenarios where manually designing neural network architectures is challenging or time-consuming.
		+
		+

← Older revision		Revision as of 17:03, 6 August 2023
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−	<center><img src="http://i.imgur.com/dLPm3Lx.jpg" width="600">'http://i.imgur.com/dLPm3Lx.jpg'</center>	+	<center><img src="http://i.imgur.com/dLPm3Lx.jpg" width="600"><br>'http://i.imgur.com/dLPm3Lx.jpg'</center>

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 * [http://en.wikipedia.org/wiki/Neuroevolution NeuroEvolution | Wikipedia]
 * [[Evolutionary Computation / Genetic Algorithms]]
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 <youtube>mBmj8n_u2IQ</youtube>
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 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
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-[http://www.youtube.com/results?search_query=NeuroEvolution+Augmenting+Topologies+NEAT+neural+artificial+intelligence Youtube search...]
+[https://www.youtube.com/results?search_query=ai+NeuroEvolution+Augmenting+Topologies+NEAT+Evolution YouTube]
-[http://www.google.com/search?q=NeuroEvolution+Augmenting+Topologies+NEAT+machine+learning+ML ...Google search]
+[https://www.quora.com/search?q=ai%20NeuroEvolution%20Augmenting%20Topologies%20NEAT%20Evolution ... Quora]
 * [http://wagenaartje.github.io/neataptic/docs/neat/ NEAT | Neataptic]
 * [http://www.cs.ucf.edu/~kstanley/neat.html The NeuroEvolution of Augmenting Topologies (NEAT) Users Page]

← Older revision		Revision as of 03:52, 3 February 2019
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		+	{{#seo:
		+	\|title=PRIMO.ai
		+	\|titlemode=append
		+	\|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
		+	\|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
		+	}}
	[http://www.youtube.com/results?search_query=NeuroEvolution+Augmenting+Topologies+NEAT+neural+artificial+intelligence Youtube search...]		[http://www.youtube.com/results?search_query=NeuroEvolution+Augmenting+Topologies+NEAT+neural+artificial+intelligence Youtube search...]
	[http://www.google.com/search?q=NeuroEvolution+Augmenting+Topologies+NEAT+machine+learning+ML ...Google search]		[http://www.google.com/search?q=NeuroEvolution+Augmenting+Topologies+NEAT+machine+learning+ML ...Google search]

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 [http://www.google.com/search?q=NeuroEvolution+Augmenting+Topologies+NEAT+machine+learning+ML ...Google search]
 * [[Architectures]]
 * [http://en.wikipedia.org/wiki/Neuroevolution NeuroEvolution | Wikipedia]
 * [[Evolutionary Computation / Genetic Algorithms]]
-http://cdn-images-1.medium.com/max/600/1*SrhRHdHjTDkAbq8W-ZMfSQ.png
+http://i.imgur.com/dLPm3Lx.jpg
-<youtube>kHyNqSnzP8Y</youtube>
+<youtube>H4WnRLEG73Q</youtube>