PICT 6 Natural Computing and Beyond 1st edition by Yasuhiro Suzuki, Toshiyuki Nakagaki ISBN B077Z7W2TK  978-4431543930

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ISBN 10: B077Z7W2TK
ISBN 13: 978-4431543930
Author: Yasuhiro Suzuki, Toshiyuki Nakagaki 

This book contains the joint proceedings of the Winter School of Hakodate (WSH) 2011 held in Hakodate, Japan, March 15–16, 2011, and the 6th International Workshop on Natural Computing (6th IWNC) held in Tokyo, Japan, March 28–30, 2012, organized by the Special Interest Group of Natural Computing (SIG-NAC), the Japanese Society for Artificial Intelligence (JSAI). This volume compiles refereed contributions to various aspects of natural computing, ranging from computing with slime mold, artificial chemistry, eco-physics, and synthetic biology, to computational aesthetics.

PICT 6 – Natural Computing and Beyond 1st Table of contents:

Preface

• Introduction to PICT 6 and the Topic of Natural Computing
• Purpose and Scope of the Book
• Overview of the Themes and Research Areas Covered

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Part 1: Foundations of Natural Computing

Chapter 1: Introduction to Natural Computing

• Overview of Natural Computing Concepts
• History and Evolution of Natural Computing
• Key Techniques in Natural Computing
• Applications and Impact of Natural Computing on Science and Technology

Chapter 2: Biological and Natural Inspirations in Computing

• Natural Systems and Their Relevance to Computing
• Principles from Evolution, Genetics, and Ecology
• Analogies Between Biological Systems and Computational Models

Chapter 3: Computational Models Inspired by Nature

• Genetic Algorithms and Evolutionary Computation
• Swarm Intelligence and Ant Colony Optimization
• Artificial Life and Cellular Automata

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Part 2: Techniques and Methods in Natural Computing

Chapter 4: Evolutionary Algorithms

• Genetic Algorithms and Their Applications
• Genetic Programming
• Evolution Strategies and Differential Evolution

Chapter 5: Swarm Intelligence

• Particle Swarm Optimization (PSO)
• Ant Colony Optimization (ACO)
• Firefly Algorithm and Other Swarm Models
• Applications of Swarm Intelligence in Optimization and Learning

Chapter 6: Artificial Life and Cellular Automata

• The Concept of Artificial Life (A-Life)
• Cellular Automata: From Conway’s Game of Life to Modern Applications
• Applications of A-Life Models in Problem Solving

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Part 3: Advanced Topics in Natural Computing

Chapter 7: Quantum Computing and Natural Computation

• The Intersection of Quantum Computing and Natural Systems
• Quantum Algorithms Inspired by Biological Systems
• Challenges and Opportunities in Quantum Natural Computing

Chapter 8: DNA Computing and Biocomputing

• Introduction to DNA Computing
• Computational Models Based on DNA Manipulation
• Biological Computing and Its Future Potential

Chapter 9: Membrane Computing and P Systems

• Overview of Membrane Computing (P Systems)
• Applications of P Systems in Solving Computational Problems
• Theoretical Foundations and Practical Use of Membrane Systems

Chapter 10: Hybrid Models in Natural Computing

• Combining Evolutionary Algorithms with Swarm Intelligence
• Hybrid Systems for Complex Problem Solving
• Multi-Agent Systems and Their Relation to Natural Computing

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Part 4: Applications of Natural Computing

Chapter 11: Natural Computing in Optimization Problems

• Applying Evolutionary Algorithms and Swarm Intelligence to Optimization
• Case Studies in Engineering, Economics, and Logistics
• Solving Large-Scale Optimization Problems

Chapter 12: Machine Learning and Data Mining

• Natural Computing Approaches to Machine Learning
• Evolutionary Learning and Swarm-Based Learning Algorithms
• Data Mining Using Evolutionary Algorithms and Genetic Programming

Chapter 13: Natural Computing in Artificial Intelligence

• Natural Computing Techniques in AI Systems
• Knowledge Representation and Reasoning Using Evolutionary Algorithms
• Applications in Robotics, Perception, and Decision Making

Chapter 14: Natural Computing for Bioinformatics and Computational Biology

• DNA Computing in Genomic Research
• Swarm Intelligence for Biological Data Analysis
• Evolutionary Algorithms in Protein Folding and Molecular Simulation

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Part 5: The Future of Natural Computing

Chapter 15: Challenges in Natural Computing

• Scalability and Efficiency of Natural Computing Models
• Dealing with Noise and Uncertainty in Natural Computing Systems
• Theoretical Limitations and Open Problems

Chapter 16: Emerging Trends and Future Directions

• The Role of Natural Computing in the Future of AI and Robotics
• Convergence with Other Computing Paradigms (Quantum, Neuromorphic)
• Ethical Considerations in Biologically Inspired Computing Systems

Chapter 17: Conclusion: Natural Computing in the 21st Century

• Summing Up Key Insights
• The Continuing Evolution of Natural Computing Techniques
• Vision for the Future of Natural Computing Research

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Appendices

• A. Mathematical Foundations of Evolutionary Algorithms and Swarm Intelligence
• B. Tools and Software for Natural Computing
• C. Resources for Further Study and Research

References

Index

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