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The implementation of evolvable-substrate HyperNEAT algorithm in GO language. ES-HyperNEAT is an extension of the original HyperNEAT method for evolving large-scale artificial neural networks.

License: MIT License

Go 98.81% Makefile 1.19%

artifical-neural-network explainable-artificial-intelligence golang hyperneat modular-ai neat neuroevolution reinforcement-learning unsupervised-learning-algorithms unsupervised-machine-learning

goeshyperneat's Introduction

📖 My Books

Hands-On Neuroevolution with Python: Build high-performing artificial neural network architectures using neuroevolution-based algorithms. ISBN: 978-1-838-82491-4 ( Packt | O’Reilly | Google Books | Amazon )
Machine Learning and the City. Chapter 12 Autonomous Artificial Intelligent Agents. DOI: 10.1002/9781119815075.ch21 part of ISBN: 978-1-119-74963-9 ( Wiley-Blackwell | Wiley Online Library | Google Books | Amazon )

📑 My Papers

Omelianenko, Iaroslav. Applying Deep Machine Learning for Psycho-Demographic Profiling of Internet Users Using O.C.E.A.N. Model of Personality. arXiv [Cs.LG], 2017. P. 1–24. DOI: https://doi.org/10.48550/arxiv.1703.06914.
Omelianenko, Iaroslav. Applying Advanced Machine Learning Models to Classify Electro-Physiological Activity of Human Brain for Use in Biometric Identification. arXiv [Cs.LG], 2017. P. 1–8. DOI: 10.48550/arxiv.1708.01167
Omelianenko, I. Creation of Autonomous Artificial Intelligent Agents using Novelty Search method of fitness function optimization. Hal.science, 2018. P. 1–7. URL: https://hal.science/hal-01868756
Omelianeko, I. Artificial Swarm Intelligence and Cooperative Robotic Systems. Preprints.org, 2019, P. 1–8. DOI: 10.20944/preprints201901.0282.v1
Omelianeko, I. Simulation of the autonomous maze navigation using the NEAT algorithm. Problems in programming, 2023. Vol. 4, P. 76–89. DOI: 10.15407/pp2023.04.076
Omelianenko, I. and Sinitsyn, I. Artificial swarm intelligence. International Scientific Technical Journal "Problems of Control and Informatics", 2024, Vol. 69. N 3. P. 91–103. DOI: 10.34229/1028-0979-2024-3-7

🔭 Researcher Profiles

goeshyperneat's People

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chrlns elmware sikora507

goeshyperneat's Issues

Add substrate layout serialisation using Cytoscape JSON encoding

Finish ESHyperNEAT implementation

initial_depth or maximal_depth > 3 causes timeout error within a few generations

changing the maximal_depth parameter in retina/es_hyper.neat.yml away from 3 to 5 for example will freeze within a few generations, and usually causing a killed signal sent to the program. This is pretty important since, in the es-HyperNEAT paper, the researchers set the maximal_depth to 32x32 for the retina experiment to reflect the complexity of the problem.

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