Natural Computing Algorithms

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.

• 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.

“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)