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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
Languages
Python
Concepts
Neural Networks
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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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Exercises

  1. Try to increase the compatibility_disjoint_coefficient parameter in the maze_config.ini file and run the experiment with new settings. What impact does this modification have on the number of species produced during the evolution? Is the neuroevolution process able to find a successful maze solver?
  2. Increase the population size by 200% (the pop_size parameter). Was the neuroevolution process able to find a solution in this case, and if so, how many generations did it take?
  3. Change the seed value of the random number generator (see line 118 of the maze_experiment.py file). Does the neuroevolution process succeed with this new value?
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Hands-On Neuroevolution with Python.
Published in: Dec 2019Publisher: PacktISBN-13: 9781838824914
© 2019 Packt Publishing Limited All Rights Reserved

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

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