### Basic Concepts, Algorithms, and Applications

### Basic Concepts, Algorithms, and Applications

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Natural computing brings together nature and computing to develop new computational tools for problem solving; to synthesize natural patterns and behaviors in computers; and to potentially design novel types of computers. Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications presents a wide-ranging survey of novel techniques and important applications of nature-based computing.

This book presents theoretical and philosophical discussions, pseudocodes for algorithms, and computing paradigms that illustrate how computational techniques can be used to solve complex problems, simulate nature, explain natural phenomena, and possibly allow the development of new computing technologies. The author features a consistent and approachable, textbook-style format that includes lucid figures, tables, real-world examples, and different types of exercises that complement the concepts while encouraging readers to apply the computational tools in each chapter. Building progressively upon core concepts of nature-inspired techniques, the topics include evolutionary computing, neurocomputing, swarm intelligence, immunocomputing, fractal geometry, artificial life, quantum computing, and DNA computing.

Fundamentals of Natural Computing is a self-contained introduction and a practical guide to nature-based computational approaches that will find numerous applications in a variety of growing fields including engineering, computer science, biological modeling, and bioinformatics.

Introduction

A Small Sample of Ideas

The Philosophy of Natural Computing

The Three Branches: A Brief Overview

When to Use Natural Computing Approaches

Conceptualization

General Concepts

PART I - COMPUTING INSPIRED BY NATURE

Evolutionary Computing

Problem Solving as a Search Task

Hill Climbing and Simulated Annealing

Evolutionary Biology

Evolutionary Computing

The Other Main Evolutionary Algorithms

From Evolutionary Biology to Computing

Scope of Evolutionary Computing

Neurocomputing

The Nervous System

Artificial Neural Networks

Typical ANNS and Learning Algorithms

From Natural to Artificial Neural Networks

Scope of Neurocomputing

Swarm Intelligence

Ant Colonies

Swarm Robotics

Social Adaptation of Knowledge

Immunocomputing

The Immune System

Artificial Immune Systems

Bone Marrow Models

Negative Selection Algorithms

Clonal Selection and Affinity Maturation

Artificial Immune Networks

From Natural to Artificial Immune Systems

Scope of Artificial Immune Systems

PART II - SIMULATION AND EMULATION OF NATURAL PHENOMENA IN COMPUTERS

Fractal Geometry of Nature

The Fractal Geometry of Nature

Cellular Automata

L-Systems

Iterated Function Systems

Fractional Brownian Motion

Particle Systems

Evolving the Geometry of Nature

From Natural to Fractal Geometry

Artificial Life

Concepts and Features of Artificial Life Systems

Examples of Artificial Life Projects

Scope of Artificial Life

From Artificial Life to Life-As-We-Know-It

PART III - COMPUTING WITH NATURAL MATERIALS

DNA Computing

Basic Concepts from Molecular Biology

Filtering Models

Formal Models: A Brief Description

Universal DNA Computers

Scope of DNA Computing

From Classical to DNA Computing

Quantum Computing

Basic Concepts from Quantum Theory

Principles from Quantum Mechanics

Quantum Information

Universal Quantum Computers

Quantum Algorithms

Physical Realizations of Quantum Computers: A Brief Description

Scope of Quantum Computing

From Classical to Quantum Computing

Afterwords

New Prospects

The Growth of Natural Computing

Some Lessons from Natural Computing

Artificial Intelligence and Natural Computing

Visions

Appendix A: Glossary of Terms

Appendix B: Theoretical Background

Linear Algebra

Statistics

Theory of Computation and Complexity

Other Concepts

Bibliography

Appendix C: A Quick Guide to the Literature

Introduction

Conceptualization

Evolutionary Computing

Neurocomputing

Swarm Intelligence

Immunocomputing

Fractal Geometry of Nature

Artificial Life

DNA Computing

Quantum Computing

Index

*All Chapters contain an Introduction, Summaries, Discussions, Exercises, and References

Natural computing brings together nature and computing to develop new computational tools for problem solving; to synthesize natural patterns and behaviors in computers; and to potentially design novel types of computers. Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications presents a wide-ranging survey of novel techniques and important applications of nature-based computing.

This book presents theoretical and philosophical discussions, pseudocodes for algorithms, and computing paradigms that illustrate how computational techniques can be used to solve complex problems, simulate nature, explain natural phenomena, and possibly allow the development of new computing technologies. The author features a consistent and approachable, textbook-style format that includes lucid figures, tables, real-world examples, and different types of exercises that complement the concepts while encouraging readers to apply the computational tools in each chapter. Building progressively upon core concepts of nature-inspired techniques, the topics include evolutionary computing, neurocomputing, swarm intelligence, immunocomputing, fractal geometry, artificial life, quantum computing, and DNA computing.

Fundamentals of Natural Computing is a self-contained introduction and a practical guide to nature-based computational approaches that will find numerous applications in a variety of growing fields including engineering, computer science, biological modeling, and bioinformatics.

Introduction

A Small Sample of Ideas

The Philosophy of Natural Computing

The Three Branches: A Brief Overview

When to Use Natural Computing Approaches

Conceptualization

General Concepts

PART I - COMPUTING INSPIRED BY NATURE

Evolutionary Computing

Problem Solving as a Search Task

Hill Climbing and Simulated Annealing

Evolutionary Biology

Evolutionary Computing

The Other Main Evolutionary Algorithms

From Evolutionary Biology to Computing

Scope of Evolutionary Computing

Neurocomputing

The Nervous System

Artificial Neural Networks

Typical ANNS and Learning Algorithms

From Natural to Artificial Neural Networks

Scope of Neurocomputing

Swarm Intelligence

Ant Colonies

Swarm Robotics

Social Adaptation of Knowledge

Immunocomputing

The Immune System

Artificial Immune Systems

Bone Marrow Models

Negative Selection Algorithms

Clonal Selection and Affinity Maturation

Artificial Immune Networks

From Natural to Artificial Immune Systems

Scope of Artificial Immune Systems

PART II - SIMULATION AND EMULATION OF NATURAL PHENOMENA IN COMPUTERS

Fractal Geometry of Nature

The Fractal Geometry of Nature

Cellular Automata

L-Systems

Iterated Function Systems

Fractional Brownian Motion

Particle Systems

Evolving the Geometry of Nature

From Natural to Fractal Geometry

Artificial Life

Concepts and Features of Artificial Life Systems

Examples of Artificial Life Projects

Scope of Artificial Life

From Artificial Life to Life-As-We-Know-It

PART III - COMPUTING WITH NATURAL MATERIALS

DNA Computing

Basic Concepts from Molecular Biology

Filtering Models

Formal Models: A Brief Description

Universal DNA Computers

Scope of DNA Computing

From Classical to DNA Computing

Quantum Computing

Basic Concepts from Quantum Theory

Principles from Quantum Mechanics

Quantum Information

Universal Quantum Computers

Quantum Algorithms

Physical Realizations of Quantum Computers: A Brief Description

Scope of Quantum Computing

From Classical to Quantum Computing

Afterwords

New Prospects

The Growth of Natural Computing

Some Lessons from Natural Computing

Artificial Intelligence and Natural Computing

Visions

Appendix A: Glossary of Terms

Appendix B: Theoretical Background

Linear Algebra

Statistics

Theory of Computation and Complexity

Other Concepts

Bibliography

Appendix C: A Quick Guide to the Literature

Introduction

Conceptualization

Evolutionary Computing

Neurocomputing

Swarm Intelligence

Immunocomputing

Fractal Geometry of Nature

Artificial Life

DNA Computing

Quantum Computing

Index

*All Chapters contain an Introduction, Summaries, Discussions, Exercises, and References

Natural computing brings together nature and computing to develop new computational tools for problem solving; to synthesize natural patterns and behaviors in computers; and to potentially design novel types of computers. Fundamentals of Natural Computing: Basic Concepts, Algorithms, and Applications presents a wide-ranging survey of novel techniques and important applications of nature-based computing.

This book presents theoretical and philosophical discussions, pseudocodes for algorithms, and computing paradigms that illustrate how computational techniques can be used to solve complex problems, simulate nature, explain natural phenomena, and possibly allow the development of new computing technologies. The author features a consistent and approachable, textbook-style format that includes lucid figures, tables, real-world examples, and different types of exercises that complement the concepts while encouraging readers to apply the computational tools in each chapter. Building progressively upon core concepts of nature-inspired techniques, the topics include evolutionary computing, neurocomputing, swarm intelligence, immunocomputing, fractal geometry, artificial life, quantum computing, and DNA computing.

Fundamentals of Natural Computing is a self-contained introduction and a practical guide to nature-based computational approaches that will find numerous applications in a variety of growing fields including engineering, computer science, biological modeling, and bioinformatics.

Introduction

A Small Sample of Ideas

The Philosophy of Natural Computing

The Three Branches: A Brief Overview

When to Use Natural Computing Approaches

Conceptualization

General Concepts

PART I - COMPUTING INSPIRED BY NATURE

Evolutionary Computing

Problem Solving as a Search Task

Hill Climbing and Simulated Annealing

Evolutionary Biology

Evolutionary Computing

The Other Main Evolutionary Algorithms

From Evolutionary Biology to Computing

Scope of Evolutionary Computing

Neurocomputing

The Nervous System

Artificial Neural Networks

Typical ANNS and Learning Algorithms

From Natural to Artificial Neural Networks

Scope of Neurocomputing

Swarm Intelligence

Ant Colonies

Swarm Robotics

Social Adaptation of Knowledge

Immunocomputing

The Immune System

Artificial Immune Systems

Bone Marrow Models

Negative Selection Algorithms

Clonal Selection and Affinity Maturation

Artificial Immune Networks

From Natural to Artificial Immune Systems

Scope of Artificial Immune Systems

PART II - SIMULATION AND EMULATION OF NATURAL PHENOMENA IN COMPUTERS

Fractal Geometry of Nature

The Fractal Geometry of Nature

Cellular Automata

L-Systems

Iterated Function Systems

Fractional Brownian Motion

Particle Systems

Evolving the Geometry of Nature

From Natural to Fractal Geometry

Artificial Life

Concepts and Features of Artificial Life Systems

Examples of Artificial Life Projects

Scope of Artificial Life

From Artificial Life to Life-As-We-Know-It

PART III - COMPUTING WITH NATURAL MATERIALS

DNA Computing

Basic Concepts from Molecular Biology

Filtering Models

Formal Models: A Brief Description

Universal DNA Computers

Scope of DNA Computing

From Classical to DNA Computing

Quantum Computing

Basic Concepts from Quantum Theory

Principles from Quantum Mechanics

Quantum Information

Universal Quantum Computers

Quantum Algorithms

Physical Realizations of Quantum Computers: A Brief Description

Scope of Quantum Computing

From Classical to Quantum Computing

Afterwords

New Prospects

The Growth of Natural Computing

Some Lessons from Natural Computing

Artificial Intelligence and Natural Computing

Visions

Appendix A: Glossary of Terms

Appendix B: Theoretical Background

Linear Algebra

Statistics

Theory of Computation and Complexity

Other Concepts

Bibliography

Appendix C: A Quick Guide to the Literature

Introduction

Conceptualization

Evolutionary Computing

Neurocomputing

Swarm Intelligence

Immunocomputing

Fractal Geometry of Nature

Artificial Life

DNA Computing

Quantum Computing

Index

*All Chapters contain an Introduction, Summaries, Discussions, Exercises, and References

Introduction

A Small Sample of Ideas

The Philosophy of Natural Computing

The Three Branches: A Brief Overview

When to Use Natural Computing Approaches

Conceptualization

General Concepts

PART I - COMPUTING INSPIRED BY NATURE

Evolutionary Computing

Problem Solving as a Search Task

Hill Climbing and Simulated Annealing

Evolutionary Biology

Evolutionary Computing

The Other Main Evolutionary Algorithms

From Evolutionary Biology to Computing

Scope of Evolutionary Computing

Neurocomputing

The Nervous System

Artificial Neural Networks

Typical ANNS and Learning Algorithms

From Natural to Artificial Neural Networks

Scope of Neurocomputing

Swarm Intelligence

Ant Colonies

Swarm Robotics

Social Adaptation of Knowledge

Immunocomputing

The Immune System

Artificial Immune Systems

Bone Marrow Models

Negative Selection Algorithms

Clonal Selection and Affinity Maturation

Artificial Immune Networks

From Natural to Artificial Immune Systems

Scope of Artificial Immune Systems

PART II - SIMULATION AND EMULATION OF NATURAL PHENOMENA IN COMPUTERS

Fractal Geometry of Nature

The Fractal Geometry of Nature

Cellular Automata

L-Systems

Iterated Function Systems

Fractional Brownian Motion

Particle Systems

Evolving the Geometry of Nature

From Natural to Fractal Geometry

Artificial Life

Concepts and Features of Artificial Life Systems

Examples of Artificial Life Projects

Scope of Artificial Life

From Artificial Life to Life-As-We-Know-It

PART III - COMPUTING WITH NATURAL MATERIALS

DNA Computing

Basic Concepts from Molecular Biology

Filtering Models

Formal Models: A Brief Description

Universal DNA Computers

Scope of DNA Computing

From Classical to DNA Computing

Quantum Computing

Basic Concepts from Quantum Theory

Principles from Quantum Mechanics

Quantum Information

Universal Quantum Computers

Quantum Algorithms

Physical Realizations of Quantum Computers: A Brief Description

Scope of Quantum Computing

From Classical to Quantum Computing

Afterwords

New Prospects

The Growth of Natural Computing

Some Lessons from Natural Computing

Artificial Intelligence and Natural Computing

Visions

Appendix A: Glossary of Terms

Appendix B: Theoretical Background

Linear Algebra

Statistics

Theory of Computation and Complexity

Other Concepts

Bibliography

Appendix C: A Quick Guide to the Literature

Introduction

Conceptualization

Evolutionary Computing

Neurocomputing

Swarm Intelligence

Immunocomputing

Fractal Geometry of Nature

Artificial Life

DNA Computing

Quantum Computing

Index

*All Chapters contain an Introduction, Summaries, Discussions, Exercises, and References

Introduction

A Small Sample of Ideas

The Philosophy of Natural Computing

The Three Branches: A Brief Overview

When to Use Natural Computing Approaches

Conceptualization

General Concepts

PART I - COMPUTING INSPIRED BY NATURE

Evolutionary Computing

Problem Solving as a Search Task

Hill Climbing and Simulated Annealing

Evolutionary Biology

Evolutionary Computing

The Other Main Evolutionary Algorithms

From Evolutionary Biology to Computing

Scope of Evolutionary Computing

Neurocomputing

The Nervous System

Artificial Neural Networks

Typical ANNS and Learning Algorithms

From Natural to Artificial Neural Networks

Scope of Neurocomputing

Swarm Intelligence

Ant Colonies

Swarm Robotics

Social Adaptation of Knowledge

Immunocomputing

The Immune System

Artificial Immune Systems

Bone Marrow Models

Negative Selection Algorithms

Clonal Selection and Affinity Maturation

Artificial Immune Networks

From Natural to Artificial Immune Systems

Scope of Artificial Immune Systems

PART II - SIMULATION AND EMULATION OF NATURAL PHENOMENA IN COMPUTERS

Fractal Geometry of Nature

The Fractal Geometry of Nature

Cellular Automata

L-Systems

Iterated Function Systems

Fractional Brownian Motion

Particle Systems

Evolving the Geometry of Nature

From Natural to Fractal Geometry

Artificial Life

Concepts and Features of Artificial Life Systems

Examples of Artificial Life Projects

Scope of Artificial Life

From Artificial Life to Life-As-We-Know-It

PART III - COMPUTING WITH NATURAL MATERIALS

DNA Computing

Basic Concepts from Molecular Biology

Filtering Models

Formal Models: A Brief Description

Universal DNA Computers

Scope of DNA Computing

From Classical to DNA Computing

Quantum Computing

Basic Concepts from Quantum Theory

Principles from Quantum Mechanics

Quantum Information

Universal Quantum Computers

Quantum Algorithms

Physical Realizations of Quantum Computers: A Brief Description

Scope of Quantum Computing

From Classical to Quantum Computing

Afterwords

New Prospects

The Growth of Natural Computing

Some Lessons from Natural Computing

Artificial Intelligence and Natural Computing

Visions

Appendix A: Glossary of Terms

Appendix B: Theoretical Background

Linear Algebra

Statistics

Theory of Computation and Complexity

Other Concepts

Bibliography

Appendix C: A Quick Guide to the Literature

Introduction

Conceptualization

Evolutionary Computing

Neurocomputing

Swarm Intelligence

Immunocomputing

Fractal Geometry of Nature

Artificial Life

DNA Computing

Quantum Computing

Index

*All Chapters contain an Introduction, Summaries, Discussions, Exercises, and References

Introduction

A Small Sample of Ideas

The Philosophy of Natural Computing

The Three Branches: A Brief Overview

When to Use Natural Computing Approaches

Conceptualization

General Concepts

PART I - COMPUTING INSPIRED BY NATURE

Evolutionary Computing

Problem Solving as a Search Task

Hill Climbing and Simulated Annealing

Evolutionary Biology

Evolutionary Computing

The Other Main Evolutionary Algorithms

From Evolutionary Biology to Computing

Scope of Evolutionary Computing

Neurocomputing

The Nervous System

Artificial Neural Networks

Typical ANNS and Learning Algorithms

From Natural to Artificial Neural Networks

Scope of Neurocomputing

Swarm Intelligence

Ant Colonies

Swarm Robotics

Social Adaptation of Knowledge

Immunocomputing

The Immune System

Artificial Immune Systems

Bone Marrow Models

Negative Selection Algorithms

Clonal Selection and Affinity Maturation

Artificial Immune Networks

From Natural to Artificial Immune Systems

Scope of Artificial Immune Systems

PART II - SIMULATION AND EMULATION OF NATURAL PHENOMENA IN COMPUTERS

Fractal Geometry of Nature

The Fractal Geometry of Nature

Cellular Automata

L-Systems

Iterated Function Systems

Fractional Brownian Motion

Particle Systems

Evolving the Geometry of Nature

From Natural to Fractal Geometry

Artificial Life

Concepts and Features of Artificial Life Systems

Examples of Artificial Life Projects

Scope of Artificial Life

From Artificial Life to Life-As-We-Know-It

PART III - COMPUTING WITH NATURAL MATERIALS

DNA Computing

Basic Concepts from Molecular Biology

Filtering Models

Formal Models: A Brief Description

Universal DNA Computers

Scope of DNA Computing

From Classical to DNA Computing

Quantum Computing

Basic Concepts from Quantum Theory

Principles from Quantum Mechanics

Quantum Information

Universal Quantum Computers

Quantum Algorithms

Physical Realizations of Quantum Computers: A Brief Description

Scope of Quantum Computing

From Classical to Quantum Computing

Afterwords

New Prospects

The Growth of Natural Computing

Some Lessons from Natural Computing

Artificial Intelligence and Natural Computing

Visions

Appendix A: Glossary of Terms

Appendix B: Theoretical Background

Linear Algebra

Statistics

Theory of Computation and Complexity

Other Concepts

Bibliography

Appendix C: A Quick Guide to the Literature

Introduction

Conceptualization

Evolutionary Computing

Neurocomputing

Swarm Intelligence

Immunocomputing

Fractal Geometry of Nature

Artificial Life

DNA Computing

Quantum Computing

Index

*All Chapters contain an Introduction, Summaries, Discussions, Exercises, and References