Learn to implement genetic and evolutionary algorithms to solve problems in a pythonic way.

Natural Computing is the field of research inspired by nature, that allows the development of new algorithms to solve complex problems, leads to the synthesis of natural models, and may result in the design of new computing systems. This book exactly aims to educate you with practical examples of topics of importance associated with the research field of Natural computing.
 
The initial few chapters will quickly walk you through Neural Networks while describing deep learning architectures such as CNN, RNN, and AutoEncoders using Keras. As you progress further, you’ll gain understanding to develop a genetic algorithm to solve traveling salesman problems, implement swarm intelligence techniques using the SwarmPackagePy and Cellular Automata techniques such as Game of Life, Langton's ant, etc. 
 
The latter half of the book will introduce you to the world of Fractals such as the Cantor Set and the Mandelbrot Set, develop a quantum program with the QiSkit tool that runs on a real quantum computing platform, namely the IBM Q Machine and a Python simulation of the Adleman experiment that showed for the first time the possibility of performing computations at the molecular level.
 
Tagline
A step-by-step guide to learn and solve complex computational problems with Nature-Inspired algorithms.

Key Features

What Will You Learn

Who This Book is For
This book is for all science enthusiasts, in particular, who want to understand what are the links between computer sciences and natural systems. Interested readers should have good skills in math and python programming along with some basic knowledge of physics and biology. Although some knowledge of the topics covered in the book will be helpful, it is not essential to have worked with the tools covered in the book.