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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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Objective function definition using the fitness score

In this section, you will learn about the creation of successful maze-solver agents using a goal-oriented objective function to guide the evolutionary process. This objective function is based on the estimation of the fitness score of the maze solver by measuring the distance between its final position and the maze exit after executing the 400 simulation steps. Thus, the objective function is goal-oriented and solely depends on the ultimate goal of the experiment: reaching the maze exit area.

In the next chapter, we will consider a different approach for solution search optimization, which is based on the Novelty Search (NS) optimization method. The NS optimization method is built around exploring new configurations of the solver agent during evolution and doesn't include proximity to the final goal (in this case, the...

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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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