Genetic And Evolutionary Computing
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Genetic And Evolutionary Computing
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Author : Jeng-Shyang Pan
language : en
Publisher: Springer Nature
Release Date : 2020-03-12
Genetic And Evolutionary Computing written by Jeng-Shyang Pan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-12 with Technology & Engineering categories.
This book gathers papers presented at the 13th International Conference on Genetic and Evolutionary Computing (ICGEC 2019), which was held in Qingdao, China, from 1st to 3rd, November 2019. Since it was established, in 2006, the ICGEC conference series has been devoted to new approaches with a focus on evolutionary computing. Today, it is a forum for the researchers and professionals in all areas of computational intelligence including evolutionary computing, machine learning, soft computing, data mining, multimedia and signal processing, swarm intelligence and security. The book appeals to policymakers, academics, educators, researchers in pedagogy and learning theory, school teachers, and other professionals in the learning industry, and further and continuing education.
Genetic And Evolutionary Computation
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Author : Stephen L. Smith
language : en
Publisher: John Wiley & Sons
Release Date : 2011-07-26
Genetic And Evolutionary Computation written by Stephen L. Smith and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-07-26 with Science categories.
Genetic and Evolutionary Computation: Medical Applications provides an overview of the range of GEC techniques being applied to medicine and healthcare in a context that is relevant not only for existing GEC practitioners but also those from other disciplines, particularly health professionals. There is rapidly increasing interest in applying evolutionary computation to problems in medicine, but to date no text that introduces evolutionary computation in a medical context. By explaining the basic introductory theory, typical application areas and detailed implementation in one coherent volume, this book will appeal to a wide audience from software developers to medical scientists. Centred around a set of nine case studies on the application of GEC to different areas of medicine, the book offers an overview of applications of GEC to medicine, describes applications in which GEC is used to analyse medical images and data sets, derive advanced models, and suggest diagnoses and treatments, finally providing hints about possible future advancements of genetic and evolutionary computation in medicine. Explores the rapidly growing area of genetic and evolutionary computation in context of its viable and exciting payoffs in the field of medical applications. Explains the underlying theory, typical applications and detailed implementation. Includes general sections about the applications of GEC to medicine and their expected future developments, as well as specific sections on applications of GEC to medical imaging, analysis of medical data sets, advanced modelling, diagnosis and treatment. Features a wide range of tables, illustrations diagrams and photographs.
Representations For Genetic And Evolutionary Algorithms
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Author : Franz Rothlauf
language : en
Publisher: Physica
Release Date : 2012-12-06
Representations For Genetic And Evolutionary Algorithms written by Franz Rothlauf and has been published by Physica this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
In the field of genetic and evolutionary algorithms (GEAs), much theory and empirical study has been heaped upon operators and test problems, but problem representation has often been taken as given. This monograph breaks with this tradition and studies a number of critical elements of a theory of representations for GEAs and applies them to the empirical study of various important idealized test functions and problems of commercial import. The book considers basic concepts of representations, such as redundancy, scaling and locality and describes how GEAs'performance is influenced. Using the developed theory representations can be analyzed and designed in a theory-guided manner. The theoretical concepts are used as examples for efficiently solving integer optimization problems and network design problems. The results show that proper representations are crucial for GEAs'success.
Evolutionary Computation
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Author : Kenneth A. De Jong
language : en
Publisher: MIT Press
Release Date : 2006-02-03
Evolutionary Computation written by Kenneth A. De Jong and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-02-03 with Computers categories.
This text is an introduction to the field of evolutionary computation. It approaches evolution strategies and genetic programming, as instances of a more general class of evolutionary algorithms.
Introduction To Evolutionary Computing
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Author : Agoston E. Eiben
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-14
Introduction To Evolutionary Computing written by Agoston E. Eiben and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-14 with Computers categories.
Evolutionary Computing is the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. These techniques are being increasingly widely applied to a variety of problems, ranging from practical applications in industry and commerce to leading-edge scientific research. This book presents the first complete overview of this exciting field aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. To this group the book is valuable because it presents EC as something to be used rather than just studied. Last, but not least, this book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.
Frontiers Of Evolutionary Computation
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Author : Anil Menon
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-04-11
Frontiers Of Evolutionary Computation written by Anil Menon and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-04-11 with Computers categories.
Frontiers of Evolutionary Computation brings together eleven contributions by international leading researchers discussing what significant issues still remain unresolved in the field of Evolutionary Computation (Ee. They explore such topics as the role of building blocks, the balancing of exploration with exploitation, the modeling of EC algorithms, the connection with optimization theory and the role of EC as a meta-heuristic method, to name a few. The articles feature a mixture of informal discussion interspersed with formal statements, thus providing the reader an opportunity to observe a wide range of EC problems from the investigative perspective of world-renowned researchers. These prominent researchers include: Heinz M]hlenbein, Kenneth De Jong, Carlos Cotta and Pablo Moscato, Lee Altenberg, Gary A. Kochenberger, Fred Glover, Bahram Alidaee and Cesar Rego, William G. Macready, Christopher R. Stephens and Riccardo Poli, Lothar M. Schmitt, John R. Koza, Matthew J. Street and Martin A. Keane, Vivek Balaraman, Wolfgang Banzhaf and Julian Miller.
Evolutionary Algorithms For Solving Multi Objective Problems
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Author : Carlos Coello Coello
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-08-26
Evolutionary Algorithms For Solving Multi Objective Problems written by Carlos Coello Coello and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-08-26 with Computers categories.
Solving multi-objective problems is an evolving effort, and computer science and other related disciplines have given rise to many powerful deterministic and stochastic techniques for addressing these large-dimensional optimization problems. Evolutionary algorithms are one such generic stochastic approach that has proven to be successful and widely applicable in solving both single-objective and multi-objective problems. This textbook is a second edition of Evolutionary Algorithms for Solving Multi-Objective Problems, significantly expanded and adapted for the classroom. The various features of multi-objective evolutionary algorithms are presented here in an innovative and student-friendly fashion, incorporating state-of-the-art research. The book disseminates the application of evolutionary algorithm techniques to a variety of practical problems, including test suites with associated performance based on a variety of appropriate metrics, as well as serial and parallel algorithm implementations.
Genetic Programming Theory And Practice Xvii
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Author : Wolfgang Banzhaf
language : en
Publisher: Springer Nature
Release Date : 2020-05-07
Genetic Programming Theory And Practice Xvii written by Wolfgang Banzhaf and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-07 with Computers categories.
These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. In this year’s edition, the topics covered include many of the most important issues and research questions in the field, such as: opportune application domains for GP-based methods, game playing and co-evolutionary search, symbolic regression and efficient learning strategies, encodings and representations for GP, schema theorems, and new selection mechanisms.The volume includes several chapters on best practices and lessons learned from hands-on experience. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.
Genetic Algorithms Data Structures Evolution Programs
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Author : Zbigniew Michalewicz
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-06-29
Genetic Algorithms Data Structures Evolution Programs written by Zbigniew Michalewicz and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-29 with Mathematics categories.
'What does your Master teach?' asked a visitor. 'Nothing,' said the disciple. 'Then why does he give discourses?' 'He only points the way - he teaches nothing.' Anthony de Mello, One Minute Wisdom During the last three decades there has been a growing interest in algorithms which rely on analogies to natural processes. The emergence of massively par allel computers made these algorithms of practical interest. The best known algorithms in this class include evolutionary programming, genetic algorithms, evolution strategies, simulated annealing, classifier systems, and neural net works. Recently (1-3 October 1990) the University of Dortmund, Germany, hosted the First Workshop on Parallel Problem Solving from Nature [164]. This book discusses a subclass of these algorithms - those which are based on the principle of evolution (survival of the fittest). In such algorithms a popu lation of individuals (potential solutions) undergoes a sequence of unary (muta tion type) and higher order (crossover type) transformations. These individuals strive for survival: a selection scheme, biased towards fitter individuals, selects the next generation. After some number of generations, the program converges - the best individual hopefully represents the optimum solution. There are many different algorithms in this category. To underline the sim ilarities between them we use the common term "evolution programs" .
Advances In Evolutionary Computing
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Author : Ashish Ghosh
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Advances In Evolutionary Computing written by Ashish Ghosh and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
The term evolutionary computing refers to the study of the foundations and applications of certain heuristic techniques based on the principles of natural evolution; thus the aim of designing evolutionary algorithms (EAs) is to mimic some of the processes taking place in natural evolution. These algo rithms are classified into three main categories, depending more on historical development than on major functional techniques. In fact, their biological basis is essentially the same. Hence EC = GA uGP u ES uEP EC = Evolutionary Computing GA = Genetic Algorithms,GP = Genetic Programming ES = Evolution Strategies,EP = Evolutionary Programming Although the details of biological evolution are not completely understood (even nowadays), there is some strong experimental evidence to support the following points: • Evolution is a process operating on chromosomes rather than on organ isms. • Natural selection is the mechanism that selects organisms which are well adapted to the environment toreproduce more often than those which are not. • The evolutionary process takes place during the reproduction stage that includes mutation (which causes the chromosomes of offspring to be dif ferent from those of the parents) and recombination (which combines the chromosomes of the parents to produce the offspring). Based upon these features, the previously mentioned three models of evolutionary computing were independently (and almost simultaneously) de veloped. An evolutionary algorithm (EA) is an iterative and stochastic process that operates on a set of individuals (called a population).