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Hands-On Genetic Algorithms with Python

You're reading from  Hands-On Genetic Algorithms with Python

Product type Book
Published in Jan 2020
Publisher Packt
ISBN-13 9781838557744
Pages 346 pages
Edition 1st Edition
Languages
Author (1):
Eyal Wirsansky Eyal Wirsansky
Profile icon Eyal Wirsansky

Table of Contents (18) Chapters

Preface Section 1: The Basics of Genetic Algorithms
An Introduction to Genetic Algorithms Understanding the Key Components of Genetic Algorithms Section 2: Solving Problems with Genetic Algorithms
Using the DEAP Framework Combinatorial Optimization Constraint Satisfaction Optimizing Continuous Functions Section 3: Artificial Intelligence Applications of Genetic Algorithms
Enhancing Machine Learning Models Using Feature Selection Hyperparameter Tuning of Machine Learning Models Architecture Optimization of Deep Learning Networks Reinforcement Learning with Genetic Algorithms Section 4: Related Technologies
Genetic Image Reconstruction Other Evolutionary and Bio-Inspired Computation Techniques Other Books You May Enjoy

Reconstructing images with polygons

One of the most popular examples of using genetic algorithms in image processing is the reconstruction of a given image with a set of semi-transparent, overlapping shapes. Besides the fun aspect and the opportunity to gain experience in image processing, these experiments provide an excellent visual insight into the evolutionary process and could potentially lead to a better understanding of visual arts, as well as developments in image analysis and image compression.

In these image reconstruction experiments multiple variations of which can be found on the internet – a familiar image, often a famous painting or a segment of it, is used as a reference. The goal is to construct a similar image by assembling a collection of overlapping shapes, typically polygons, of varying colors and transparencies.

Here, we will address this...

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