The field of natural computing has been the focus of a substantial research effort in recent decades. One particular strand of this research concerns the development of computational algorithms using metaphorical inspiration from systems and phenomena that occur in the natural world. These naturally inspired computing algorithms have proven to be successful problem-solvers across domains as diverse as management science, bioinformatics, finance, marketing, engineering, architecture and design.

This book is a comprehensive introduction to natural computing algorithms, suitable for academic and industrial researchers and for undergraduate and graduate courses on natural computing in computer science, engineering and management science.

Suitable foundation for undergraduate and graduate courses in computer science, management science and engineering Comprehensive introduction to the key natural computing algorithms for students and practitioners Authors among leading researchers on the theory and practice of evolutionary computing paradigms

Autorentext

Prof. Anthony Brabazon is currently Associate Dean of the Smurfit Graduate School of Business, University College Dublin (UCD) and Professor of Accountancy; previous positions include Vice-Principal of Research and Innovation for the College of Business and Law, Head of Research for the School of Business and Programme Director for the Master of Accounting Degree. His primary research interests concern the development of natural computing theory and the application of related algorithms to real-world problems, particularly in the domain of business and finance and he has pioneered multidisciplinary collaborations with industry in areas such as financial mathematics, financial economics and computer science. He is cofounder and co director of the Natural Computing Research and Applications Group at UCD, among the most successful research groups dedicated to this subject. He has a bachelor's degree in commerce and a diploma in accounting, he is a qualified professional accountant andhe has postgraduate qualifications in statistics and operations research.

Prof. Michael O'Neill holds the ICON Chair of Business Analytics, is Vice-Principal for Research, Innovation & Impact in the UCD College of Business, and is a founding Director of the UCD Natural Computing Research and Applications Group, among the most successful international research groups dedicated to this subject. He is one of the inventors of Grammatical Evolution and is independently ranked as one of the top 5 researchers in Genetic Programming, with over 250 peer-reviewed publications, over 4000 citations and a H-index of 29. He has held senior positions in the key academic conference committees, journal boards and review committees in this field and he has supervised many Ph.D. and research M.Sc. projects in evolutionary computing. He has a bachelor's degree in biology and a Ph.D. in computer science.

Dr. Seán McGarraghy is the Director of the UCD Smurfit Graduate School of Business M.Sc. in Business Analytics. He has qualifications in electronics, mathematics and management and his teaching and academic publications cover many aspects of business analytics and operations research. Particular topics of interests include combinatorial enumeration and optimization, network algorithms, supply chain management, quadratic forms and K-theory.

Inhalt

Introduction.- Introduction to Evolutionary Computing.- Genetic Algorithms.- Extending the Genetic Algorithm.- Evolution Strategies and Evolutionary Programming.- Differential Evolution.- Genetic Programming.- Particle Swarm Algorithms.- Ant Algorithms.- Honeybee Algorithms.- Other Social Algorithms.- Bacterial Foraging Algorithms.- Neural Networks for Supervised Learning.- Neural Networks for Unsupervised Learning.- Neuroevolution.- Artificial Immune Systems.- An Introduction to Developmental and Grammatical Computing.- Grammar-Based and Developmental Genetic Programming.- Grammatical Evolution.- TAG3P and Developmental TAG3P.- Genetic Regulatory Networks.- An Introduction to Physics-Inspired Computing.- Physics-Inspired Computing Algorithms.- Quantum-Inspired Evolutionary Algorithms.- Plant-Inspired Algorithms.- Chemistry-Inspired Algorithms.- Conclusions.- References.- Index.