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© 2015

Natural Computing Algorithms

Textbook

Part of the Natural Computing Series book series (NCS)

Table of contents

  1. Front Matter
    Pages I-XX
  2. Anthony Brabazon, Michael O’Neill, Seán McGarraghy
    Pages 1-13
  3. Evolutionary Computing

    1. Front Matter
      Pages 15-15
    2. Anthony Brabazon, Michael O’Neill, Seán McGarraghy
      Pages 17-20
    3. Anthony Brabazon, Michael O’Neill, Seán McGarraghy
      Pages 21-42
    4. Anthony Brabazon, Michael O’Neill, Seán McGarraghy
      Pages 43-71
    5. Anthony Brabazon, Michael O’Neill, Seán McGarraghy
      Pages 73-82
    6. Anthony Brabazon, Michael O’Neill, Seán McGarraghy
      Pages 83-93
    7. Anthony Brabazon, Michael O’Neill, Seán McGarraghy
      Pages 95-114
  4. Social Computing

    1. Front Matter
      Pages 115-115
    2. Anthony Brabazon, Michael O’Neill, Seán McGarraghy
      Pages 117-140
    3. Anthony Brabazon, Michael O’Neill, Seán McGarraghy
      Pages 141-170
    4. Anthony Brabazon, Michael O’Neill, Seán McGarraghy
      Pages 171-186
    5. Anthony Brabazon, Michael O’Neill, Seán McGarraghy
      Pages 187-199
    6. Anthony Brabazon, Michael O’Neill, Seán McGarraghy
      Pages 201-218
  5. Neurocomputing

    1. Front Matter
      Pages 219-219
    2. Anthony Brabazon, Michael O’Neill, Seán McGarraghy
      Pages 221-259
    3. Anthony Brabazon, Michael O’Neill, Seán McGarraghy
      Pages 261-280
    4. Anthony Brabazon, Michael O’Neill, Seán McGarraghy
      Pages 281-298
  6. Immunocomputing

    1. Front Matter
      Pages 299-299

About this book

Introduction

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.

Keywords

Artificial Immune Systems (AIS) Bacterial Foraging Algorithms Chemistry-Inspired Algorithms Developmental Computing Differential Evolution (DE) Evolution Strategies (ES) Evolutionary Algorithms (EA) Evolutionary Computing (EC) Evolutionary Programming (EP) Genetic Algorithms (GA) Genetic Programming (GP) Genetic Regulatory Networks (GRN) Grammatical Computing Grammatical Evolution (GE) Learning Metaheuristics Neural Networks Neuroevolution Optimization Particle Swarm Algorithms Physics-Inspired Computing Plant-Inspired Algorithms Quantum Computing Social Algorithms

Authors and affiliations

  1. 1.Natural Comput. Res. & Appl.University College DublinDublinIreland
  2. 2.Natural Comput. Res. & Appl.University College DublinDublinIreland
  3. 3.Centre for Business Analytics Management Infor. SystemsUniversity College Dublin Quinn School of BusinessBelfield, DublinIreland

About the authors

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 and he 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.

Bibliographic information

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Reviews

“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)