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You're reading from  Hands-On Neuroevolution with Python.

Product typeBook
Published inDec 2019
Reading LevelExpert
PublisherPackt
ISBN-139781838824914
Edition1st Edition
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Author (1)
Iaroslav Omelianenko
Iaroslav Omelianenko
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Iaroslav Omelianenko

Iaroslav Omelianenko occupied the position of CTO and research director for more than a decade. He is an active member of the research community and has published several research papers at arXiv, ResearchGate, Preprints, and more. He started working with applied machine learning by developing autonomous agents for mobile games more than a decade ago. For the last 5 years, he has actively participated in research related to applying deep machine learning methods for authentication, personal traits recognition, cooperative robotics, synthetic intelligence, and more. He is an active software developer and creates open source neuroevolution algorithm implementations in the Go language.
Read more about Iaroslav Omelianenko

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Summary

In this chapter, we learned about the method of indirect encoding of the ANN topology using CPPNs. You learned about the HyperNEAT extension of the NEAT algorithm, which uses a connective CPPN to draw connectivity patterns within the substrate of the phenotype of the discriminator ANN. Also, we demonstrated how the indirect encoding scheme allows the HyperNEAT algorithm to work with large-scale ANN topologies, which is common in pattern recognition and visual discrimination tasks.

With the theoretical background we provided, you have had the chance to improve your coding skills by implementing the solution for a visual discrimination task using Python and the MultiNEAT library. Also, you learned about a new visualization method that renders the activation values of the nodes in the output layer of the discriminator ANN and how this visualization can be used to verify the...

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Hands-On Neuroevolution with Python.
Published in: Dec 2019Publisher: PacktISBN-13: 9781838824914

Author (1)

author image
Iaroslav Omelianenko

Iaroslav Omelianenko occupied the position of CTO and research director for more than a decade. He is an active member of the research community and has published several research papers at arXiv, ResearchGate, Preprints, and more. He started working with applied machine learning by developing autonomous agents for mobile games more than a decade ago. For the last 5 years, he has actively participated in research related to applying deep machine learning methods for authentication, personal traits recognition, cooperative robotics, synthetic intelligence, and more. He is an active software developer and creates open source neuroevolution algorithm implementations in the Go language.
Read more about Iaroslav Omelianenko