Part 5: Related Technologies

This part explores the application of genetic algorithms in image processing and introduces additional biologically inspired problem-solving techniques. The first chapter is dedicated to using genetic algorithms for image reconstruction, where an image is recreated using semi-transparent polygons, culminating in a genetic algorithm-based program that reconstructs a famous painting using these techniques. In the following chapter, the scope broadens to include genetic programming, NeuroEvolution of Augmenting Topologies (NEAT), and particle swarm optimization, each demonstrated through Python-based problem-solving programs. We conclude with an overview of several other computational paradigms in this field, further expanding our understanding of evolutionary computation methods.

This part contains the following chapters:

• Chapter 15, Genetic Image Reconstruction
• Chapter 16, Other Evolutionary and Bio-Inspired Computation Techniques

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