Step-by-step guide to learn and solve complex computational problems with Nature Inspired algorithms. DESCRIPTIONÊ 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, youÕll gain understanding to develop genetic algorithm to solve traveling saleman problem, implement swarm intelligence techniques using the SwarmPackagePyÊ and Cellular Automata techniques such as Game of Life, Langton's ant, etc.Ê The latter half of the book will introduce you to the world of Fractals such as 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. KEY FEATURES 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 Fractals WHAT WILL YOU LEARN Mastering Artificial Neural Networks Developing Artificial Intelligence systemsÊ Resolving complex problems with Genetic Programming and Swarm intelligence algorithms Programming Quantum Computers Exploring the mathematical world of fractals Simulating complex systems by Cellular Automata Understanding the basics of DNA computation WHO THIS BOOK IS FORÊ 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. Table of Contents Neural Networks Deep Learning Genetic Programming Swarm Intelligence Cellular Automata Fractals Quantum Computing DNA Computing

Cartesian Genetic Programming (CGP) is a highly effective and increasingly popular form of genetic programming. It represents programs in the form of directed graphs, and a particular characteristic is that it has a highly redundant genotype–phenotype mapping, in that genes can be noncoding. It has spawned a number of new forms, each improving on the efficiency, among them modular, or embedded, CGP, and self-modifying CGP. It has been applied to many problems in both computer science and applied sciences. This book contains chapters written by the leading figures in the development and application of CGP, and it will be essential reading for researchers in genetic programming and for engineers and scientists solving applications using these techniques. It will also be useful for advanced undergraduates and postgraduates seeking to understand and utilize a highly efficient form of genetic programming.

This open access book offers an initial introduction to programming for scientific and computational applications using the Python programming language. The presentation style is compact and example-based, making it suitable for students and researchers with little or no prior experience in programming. The book uses relevant examples from mathematics and the natural sciences to present programming as a practical toolbox that can quickly enable readers to write their own programs for data processing and mathematical modeling. These tools include file reading, plotting, simple text analysis, and using NumPy for numerical computations, which are fundamental building blocks of all programs in data science and computational science. At the same time, readers are introduced to the fundamental concepts of programming, including variables, functions, loops, classes, and object-oriented programming. Accordingly, the book provides a sound basis for further computer science and programming studies.

You're interested in quantum computing and machine learning. But you don't know how to get started? Let me help! Whether you just get started with quantum computing and machine learning or you're already a senior machine learning engineer, Hands-On Quantum Machine Learning With Python is your comprehensive guide to get started with Quantum Machine Learning - the use of quantum computing for the computation of machine learning algorithms. Quantum computing promises to solve problems intractable with current computing technologies. But is it fundamentally different and asks us to change the way we think. Hands-On Quantum Machine Learning With Python strives to be the perfect balance between theory taught in a textbook and the actual hands-on knowledge you'll need to implement real-world solutions. Inside this book, you will learn the basics of quantum computing and machine learning in a practical and applied manner.

Nature has long provided the inspiration for a variety of scientific discoveries in engineering, biomedicine, and computing, though only recently have these elements of nature been used directly in computational systems. Natural Computing for Simulation and Knowledge Discovery investigates the latest developments in nature-influenced technologies. Within its pages, readers will find an in-depth analysis of such advances as cryptographic solutions based on cell division, the creation and manipulation of biological computers, and particle swarm optimization techniques. Scientists, practitioners, and students in fields such as computing, mathematics, and molecular science will make use of this essential reference to explore current trends in natural computation and advance nature-inspired technologies to the next generation.

This book introduces some key problems in bioinformatics, discusses the models used to formally describe these problems, and analyzes the algorithmic approaches used to solve them. After introducing the basics of molecular biology and algorithmics, Part I explains string algorithms and alignments; Part II details the field of physical mapping and DNA sequencing; and Part III examines the application of algorithmics to the analysis of biological data. Exciting application examples include predicting the spatial structure of proteins, and computing haplotypes from genotype data. Figures, chapter summaries, detailed derivations, and examples, are provided.

All aboard The Coding Train! This beginner-friendly creative coding tutorial is designed to grow your skills in a fun, hands-on way as you build simulations of real-world phenomena with “The Coding Train” YouTube star Daniel Shiffman. What if you could re-create the awe-inspiring flocking patterns of birds or the hypnotic dance of fireflies—with code? For over a decade, The Nature of Code has empowered countless readers to do just that, bridging the gap between creative expression and programming. This innovative guide by Daniel Shiffman, creator of the beloved Coding Train, welcomes budding and seasoned programmers alike into a world where code meets playful creativity. This JavaScript-based edition of Shiffman’s groundbreaking work gently unfolds the mysteries of the natural world, turning complex topics like genetic algorithms, physics-based simulations, and neural networks into accessible and visually stunning creations. Embark on this extraordinary adventure with projects involving: A physics engine: Simulate the push and pull of gravitational attraction. Flocking birds: Choreograph the mesmerizing dance of a flock. Branching trees: Grow lifelike and organic tree structures. Neural networks: Craft intelligent systems that learn and adapt. Cellular automata: Uncover the magic of self-organizing patterns. Evolutionary algorithms: Play witness to natural selection in your code. Shiffman’s work has transformed thousands of curious minds into creators, breaking down barriers between science, art, and technology, and inviting readers to see code not just as a tool for tasks but as a canvas for boundless creativity. Whether you’re deciphering the elegant patterns of natural phenomena or crafting your own digital ecosystems, Shiffman’s guidance is sure to inform and inspire. The Nature of Code is not just about coding; it’s about looking at the natural world in a new way and letting its wonders inspire your next creation. Dive in and discover the joy of turning code into art—all while mastering coding fundamentals along the way. NOTE: All examples are written with p5.js, a JavaScript library for creative coding, and are available on the book's website.