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IEEE 754 Floating Point Pipelined Multiplier with Karatsuba for Mitigations of Area and Power Read chapter Read first chapter

2021 | OriginalPaper | Chapter

An Approach to Geometric Modeling Using Genetic Programming

Authors : Snigdhajyoti Ghosh, Damodar Goswami, Chira Ranjan Datta

Published in: Computers and Devices for Communication

Publisher: Springer Singapore

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Abstract

In this work, we ‘derived’ the famous Pythagorean theorem from the measurements of the sides of right-angled triangles with machine learning. In classical Euclidean geometry, this result is proved with rigorous geometrical argument, but we have followed a data-driven approach and got the same result without entering a single step in the domain of geometry. We used symbolic regression with genetic programming to reach the model. As far as our knowledge goes, this result is a novel one and may open up a new avenue of applying machine learning tool in geometry. We have used Python programming language 3.7 and libraries such as DEAP (v1.2) and pygraphviz. The whole project can be found on https://​github.​com/​snigdhasjg/​Pythagorean-Triplate.​git.​

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Appendix
Available only for authorised users
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Metadata
Title
An Approach to Geometric Modeling Using Genetic Programming
Authors
Snigdhajyoti Ghosh
Damodar Goswami
Chira Ranjan Datta
Copyright Year
2021
Publisher
Springer Singapore
Book
Computers and Devices for Communication

Print ISBN: 978-981-15-8365-0

Electronic ISBN: 978-981-15-8366-7

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-15-8366-7_13