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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.
List of contents
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.
About the author
Anthony Brabazon [B. Comm (UCD), DPA (UCD), Dip Stats (Dub), MS (Statistics) (Stanford), MS (Operations Research) (Stanford), MBA (Heriot-Watt), DBA (Kingston), FCA, ACMA] lectures at University College Dublin. His research interests include mathematical decision models, evolutionary computation, and the application of computational intelligence to the domain of finance. He has published in excess of 100 papers in journals, conferences and professional publications, and has been a member of the programme committee at both EuroGP and GECCO conferences, as well as acting as reviewer for several journals. He has also acted as consultant to a wide range of public and private companies in several countries. He currently serves as a member of the CCAB (Ireland) Consultative Committee on Accounting Standards, and is a former Secretary and Treasurer of the Irish Accounting and Finance Association. Prior to joining UCD, he worked in the banking sector, and for KPMG.§
Michael O'Neill [BSc. (UCD), PhD (UL)] is a lecturer in the Department of Computer Science and Information Systems at the University of Limerick. He has over 70 publications on biologically inspired algorithms (BIAs). He coauthored the Springer title "Grammatical Evolution -- Evolutionary Automatic Programming in an Arbitrary Language", Genetic Programming Series, 2003, 160 pp., ISBN 1-4020-7444-1. He is one of the two original developers of the Grammatical Evolution algorithm, research that spawned an annual invited tutorial at the largest evolutionary computation conference and an international workshop, and is also on a number of relevant organising committees (e.g., GECCO 2005). Michael is a regular reviewer for the leading evolutionary computation (EC) journals, namely IEEE Trans. on Evolutionary Computation, MIT Press's Evolutionary Computation, and Springer's Genetic Programming and Evolvable Hardware journal.
Summary
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.
Additional text
“The book is very well organized. … the book is not only suitable for beginners in natural computing, it can also serve as a valuable reference for experts. … the book can be thought of not only as a collection of algorithms illustrating many methods and tools used in natural computing, but also as a textbook covering many aspects of the area which can be used in an introductory course on natural computing.” (Miguel A. Gutiérrez-Naranjo, Mathematical Reviews, June, 2016)
“One interesting advantage of the volume is that it was prepared by and for scholars that are not necessarily in computer science. The book is definitely a good reference and a well-written and well-explained introduction to natural computing … .” (Hector Zenil, Computing Reviews, April, 2016)
“I very much enjoyed reading this book and found it to be very comprehensive, well-structured, and well-written. It provides good coverage of natural computing approaches as well as a thorough description of each algorithm with its variants. … suitable as a textbook for a graduate student course as well as a self-study guide for research students, since there are a good number of examples provided throughout. Furthermore, the algorithm descriptions, figures and tables facilitate the learning of the different concepts.” (Simone A. Ludwig, Genetic Programming and Evolvable Machines, March, 2016)
Report
"The book is very well organized. ... the book is not only suitable for beginners in natural computing, it can also serve as a valuable reference for experts. ... the book can be thought of not only as a collection of algorithms illustrating many methods and tools used in natural computing, but also as a textbook covering many aspects of the area which can be used in an introductory course on natural computing." (Miguel A. Gutiérrez-Naranjo, Mathematical Reviews, June, 2016)
"One interesting advantage of the volume is that it was prepared by and for scholars that are not necessarily in computer science. The book is definitely a good reference and a well-written and well-explained introduction to natural computing ... ." (Hector Zenil, Computing Reviews, April, 2016)
"I very much enjoyed reading this book and found it to be very comprehensive, well-structured, and well-written. It provides good coverage of natural computing approaches as well as a thorough description of each algorithm with its variants. ... suitable as a textbook for a graduate student course as well as a self-study guide for research students, since there are a good number of examples provided throughout. Furthermore, the algorithm descriptions, figures and tables facilitate the learning of the different concepts." (Simone A. Ludwig, Genetic Programming and Evolvable Machines, March, 2016)
