Natural Computing with Python: Learn to implement genetic and evolutionary algorithms to solve problems in a pythonic way
- Length: 278 pages
- Edition: 1
- Language: English
- Publisher: BPB Publications
- Publication Date: 2021-06-21
Step-by-step guide to learn and solve complex computational problems with Nature Inspired algorithms.
- Artificial Neural Networks
- Deep Learning models using Keras
- Quantum Computers and Programming
- Genetic Algorithms, CNN and RNNs
- Swarm Intelligence Systems
- Reinforcement Learning using OpenAI
- Artificial Life
- DNA computing
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 on topics of importance associated with 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 genetic algorithm to solve traveling saleman problem, 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 such as the Cantor Set and the Mandelbro 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.