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Artificial Intelligence, Evolutionary Computing and Metaheuristics

Author: Xin-She Yang

Publisher: Springer

Published: 2012-07-27

Total Pages: 797

ISBN-13: 3642296947

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Alan Turing pioneered many research areas such as artificial intelligence, computability, heuristics and pattern formation. Nowadays at the information age, it is hard to imagine how the world would be without computers and the Internet. Without Turing's work, especially the core concept of Turing Machine at the heart of every computer, mobile phone and microchip today, so many things on which we are so dependent would be impossible. 2012 is the Alan Turing year -- a centenary celebration of the life and work of Alan Turing. To celebrate Turing's legacy and follow the footsteps of this brilliant mind, we take this golden opportunity to review the latest developments in areas of artificial intelligence, evolutionary computation and metaheuristics, and all these areas can be traced back to Turing's pioneer work. Topics include Turing test, Turing machine, artificial intelligence, cryptography, software testing, image processing, neural networks, nature-inspired algorithms such as bat algorithm and cuckoo search, and multiobjective optimization and many applications. These reviews and chapters not only provide a timely snapshot of the state-of-art developments, but also provide inspiration for young researchers to carry out potentially ground-breaking research in the active, diverse research areas in artificial intelligence, cryptography, machine learning, evolutionary computation, and nature-inspired metaheuristics. This edited book can serve as a timely reference for graduates, researchers and engineers in artificial intelligence, computer sciences, computational intelligence, soft computing, optimization, and applied sciences.

Book Synopsis Artificial Intelligence, Evolutionary Computing and Metaheuristics by : Xin-She Yang

Download or read book Artificial Intelligence, Evolutionary Computing and Metaheuristics written by Xin-She Yang and published by Springer. This book was released on 2012-07-27 with total page 797 pages. Available in PDF, EPUB and Kindle. Book excerpt: Alan Turing pioneered many research areas such as artificial intelligence, computability, heuristics and pattern formation. Nowadays at the information age, it is hard to imagine how the world would be without computers and the Internet. Without Turing's work, especially the core concept of Turing Machine at the heart of every computer, mobile phone and microchip today, so many things on which we are so dependent would be impossible. 2012 is the Alan Turing year -- a centenary celebration of the life and work of Alan Turing. To celebrate Turing's legacy and follow the footsteps of this brilliant mind, we take this golden opportunity to review the latest developments in areas of artificial intelligence, evolutionary computation and metaheuristics, and all these areas can be traced back to Turing's pioneer work. Topics include Turing test, Turing machine, artificial intelligence, cryptography, software testing, image processing, neural networks, nature-inspired algorithms such as bat algorithm and cuckoo search, and multiobjective optimization and many applications. These reviews and chapters not only provide a timely snapshot of the state-of-art developments, but also provide inspiration for young researchers to carry out potentially ground-breaking research in the active, diverse research areas in artificial intelligence, cryptography, machine learning, evolutionary computation, and nature-inspired metaheuristics. This edited book can serve as a timely reference for graduates, researchers and engineers in artificial intelligence, computer sciences, computational intelligence, soft computing, optimization, and applied sciences.

Handbook of AI-based Metaheuristics

Author: Anand J. Kulkarni

Publisher: CRC Press

Published: 2021-09-01

Total Pages: 584

ISBN-13: 1000434257

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At the heart of the optimization domain are mathematical modeling of the problem and the solution methodologies. The problems are becoming larger and with growing complexity. Such problems are becoming cumbersome when handled by traditional optimization methods. This has motivated researchers to resort to artificial intelligence (AI)-based, nature-inspired solution methodologies or algorithms. The Handbook of AI-based Metaheuristics provides a wide-ranging reference to the theoretical and mathematical formulations of metaheuristics, including bio-inspired, swarm-based, socio-cultural, and physics-based methods or algorithms; their testing and validation, along with detailed illustrative solutions and applications; and newly devised metaheuristic algorithms. This will be a valuable reference for researchers in industry and academia, as well as for all Master’s and PhD students working in the metaheuristics and applications domains.

Book Synopsis Handbook of AI-based Metaheuristics by : Anand J. Kulkarni

Download or read book Handbook of AI-based Metaheuristics written by Anand J. Kulkarni and published by CRC Press. This book was released on 2021-09-01 with total page 584 pages. Available in PDF, EPUB and Kindle. Book excerpt: At the heart of the optimization domain are mathematical modeling of the problem and the solution methodologies. The problems are becoming larger and with growing complexity. Such problems are becoming cumbersome when handled by traditional optimization methods. This has motivated researchers to resort to artificial intelligence (AI)-based, nature-inspired solution methodologies or algorithms. The Handbook of AI-based Metaheuristics provides a wide-ranging reference to the theoretical and mathematical formulations of metaheuristics, including bio-inspired, swarm-based, socio-cultural, and physics-based methods or algorithms; their testing and validation, along with detailed illustrative solutions and applications; and newly devised metaheuristic algorithms. This will be a valuable reference for researchers in industry and academia, as well as for all Master’s and PhD students working in the metaheuristics and applications domains.

Metaheuristic and Evolutionary Computation: Algorithms and Applications

Author: Hasmat Malik

Publisher: Springer Nature

Published: 2020-10-08

Total Pages: 830

ISBN-13: 9811575711

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This book addresses the principles and applications of metaheuristic approaches in engineering and related fields. The first part covers metaheuristics tools and techniques such as ant colony optimization and Tabu search, and their applications to several classes of optimization problems. In turn, the book’s second part focuses on a wide variety of metaheuristics applications in engineering and/or the applied sciences, e.g. in smart grids and renewable energy. In addition, the simulation codes for the problems discussed are included in an appendix for ready reference. Intended for researchers aspiring to learn and apply metaheuristic techniques, and gathering contributions by prominent experts in the field, the book offers readers an essential introduction to metaheuristics, its theoretical aspects and applications.

Book Synopsis Metaheuristic and Evolutionary Computation: Algorithms and Applications by : Hasmat Malik

Download or read book Metaheuristic and Evolutionary Computation: Algorithms and Applications written by Hasmat Malik and published by Springer Nature. This book was released on 2020-10-08 with total page 830 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the principles and applications of metaheuristic approaches in engineering and related fields. The first part covers metaheuristics tools and techniques such as ant colony optimization and Tabu search, and their applications to several classes of optimization problems. In turn, the book’s second part focuses on a wide variety of metaheuristics applications in engineering and/or the applied sciences, e.g. in smart grids and renewable energy. In addition, the simulation codes for the problems discussed are included in an appendix for ready reference. Intended for researchers aspiring to learn and apply metaheuristic techniques, and gathering contributions by prominent experts in the field, the book offers readers an essential introduction to metaheuristics, its theoretical aspects and applications.

Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends

Author: Yin, Peng-Yeng

Publisher: IGI Global

Published: 2012-03-31

Total Pages: 446

ISBN-13: 1466602716

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"This book is a collection of the latest developments, models, and applications within the transdisciplinary fields related to metaheuristic computing, providing readers with insight into a wide range of topics such as genetic algorithms, differential evolution, and ant colony optimization"--Provided by publisher.

Book Synopsis Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends by : Yin, Peng-Yeng

Download or read book Modeling, Analysis, and Applications in Metaheuristic Computing: Advancements and Trends written by Yin, Peng-Yeng and published by IGI Global. This book was released on 2012-03-31 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book is a collection of the latest developments, models, and applications within the transdisciplinary fields related to metaheuristic computing, providing readers with insight into a wide range of topics such as genetic algorithms, differential evolution, and ant colony optimization"--Provided by publisher.

Theory and Principled Methods for the Design of Metaheuristics

Author: Yossi Borenstein

Publisher: Springer Science & Business Media

Published: 2013-12-19

Total Pages: 287

ISBN-13: 3642332064

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Metaheuristics, and evolutionary algorithms in particular, are known to provide efficient, adaptable solutions for many real-world problems, but the often informal way in which they are defined and applied has led to misconceptions, and even successful applications are sometimes the outcome of trial and error. Ideally, theoretical studies should explain when and why metaheuristics work, but the challenge is huge: mathematical analysis requires significant effort even for simple scenarios and real-life problems are usually quite complex. In this book the editors establish a bridge between theory and practice, presenting principled methods that incorporate problem knowledge in evolutionary algorithms and other metaheuristics. The book consists of 11 chapters dealing with the following topics: theoretical results that show what is not possible, an assessment of unsuccessful lines of empirical research; methods for rigorously defining the appropriate scope of problems while acknowledging the compromise between the class of problems to which a search algorithm is applied and its overall expected performance; the top-down principled design of search algorithms, in particular showing that it is possible to design algorithms that are provably good for some rigorously defined classes; and, finally, principled practice, that is reasoned and systematic approaches to setting up experiments, metaheuristic adaptation to specific problems, and setting parameters. With contributions by some of the leading researchers in this domain, this book will be of significant value to scientists, practitioners, and graduate students in the areas of evolutionary computing, metaheuristics, and computational intelligence.

Book Synopsis Theory and Principled Methods for the Design of Metaheuristics by : Yossi Borenstein

Download or read book Theory and Principled Methods for the Design of Metaheuristics written by Yossi Borenstein and published by Springer Science & Business Media. This book was released on 2013-12-19 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: Metaheuristics, and evolutionary algorithms in particular, are known to provide efficient, adaptable solutions for many real-world problems, but the often informal way in which they are defined and applied has led to misconceptions, and even successful applications are sometimes the outcome of trial and error. Ideally, theoretical studies should explain when and why metaheuristics work, but the challenge is huge: mathematical analysis requires significant effort even for simple scenarios and real-life problems are usually quite complex. In this book the editors establish a bridge between theory and practice, presenting principled methods that incorporate problem knowledge in evolutionary algorithms and other metaheuristics. The book consists of 11 chapters dealing with the following topics: theoretical results that show what is not possible, an assessment of unsuccessful lines of empirical research; methods for rigorously defining the appropriate scope of problems while acknowledging the compromise between the class of problems to which a search algorithm is applied and its overall expected performance; the top-down principled design of search algorithms, in particular showing that it is possible to design algorithms that are provably good for some rigorously defined classes; and, finally, principled practice, that is reasoned and systematic approaches to setting up experiments, metaheuristic adaptation to specific problems, and setting parameters. With contributions by some of the leading researchers in this domain, this book will be of significant value to scientists, practitioners, and graduate students in the areas of evolutionary computing, metaheuristics, and computational intelligence.

Advances in Metaheuristics for Hard Optimization

Author: Patrick Siarry

Publisher: Springer Science & Business Media

Published: 2007-12-06

Total Pages: 484

ISBN-13: 3540729607

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Many advances have recently been made in metaheuristic methods, from theory to applications. The editors, both leading experts in this field, have assembled a team of researchers to contribute 21 chapters organized into parts on simulated annealing, tabu search, ant colony algorithms, general purpose studies of evolutionary algorithms, applications of evolutionary algorithms, and metaheuristics.

Book Synopsis Advances in Metaheuristics for Hard Optimization by : Patrick Siarry

Download or read book Advances in Metaheuristics for Hard Optimization written by Patrick Siarry and published by Springer Science & Business Media. This book was released on 2007-12-06 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many advances have recently been made in metaheuristic methods, from theory to applications. The editors, both leading experts in this field, have assembled a team of researchers to contribute 21 chapters organized into parts on simulated annealing, tabu search, ant colony algorithms, general purpose studies of evolutionary algorithms, applications of evolutionary algorithms, and metaheuristics.

Metaheuristics for Machine Learning

Author: Mansour Eddaly

Publisher: Springer Nature

Published: 2023-03-13

Total Pages: 231

ISBN-13: 9811938881

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Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule mining) problems. Furthermore, automatically generating programs via metaheuristics, as a form of evolutionary computation and swarm intelligence, has now gained widespread popularity. This book investigates different ways of integrating metaheuristics into machine learning techniques, from both theoretical and practical standpoints. It explores how metaheuristics can be adapted in order to enhance machine learning tools and presents an overview of the main metaheuristic programming methods. Moreover, real-world applications are provided for illustration, e.g., in clustering, big data, machine health monitoring, underwater sonar targets, and banking.

Book Synopsis Metaheuristics for Machine Learning by : Mansour Eddaly

Download or read book Metaheuristics for Machine Learning written by Mansour Eddaly and published by Springer Nature. This book was released on 2023-03-13 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: Using metaheuristics to enhance machine learning techniques has become trendy and has achieved major successes in both supervised (classification and regression) and unsupervised (clustering and rule mining) problems. Furthermore, automatically generating programs via metaheuristics, as a form of evolutionary computation and swarm intelligence, has now gained widespread popularity. This book investigates different ways of integrating metaheuristics into machine learning techniques, from both theoretical and practical standpoints. It explores how metaheuristics can be adapted in order to enhance machine learning tools and presents an overview of the main metaheuristic programming methods. Moreover, real-world applications are provided for illustration, e.g., in clustering, big data, machine health monitoring, underwater sonar targets, and banking.

Metaheuristics in Machine Learning: Theory and Applications

Author: Diego Oliva

Publisher: Springer Nature

Published:

Total Pages: 765

ISBN-13: 3030705420

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This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities.

Book Synopsis Metaheuristics in Machine Learning: Theory and Applications by : Diego Oliva

Download or read book Metaheuristics in Machine Learning: Theory and Applications written by Diego Oliva and published by Springer Nature. This book was released on with total page 765 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and deep learning. The chapters were classified based on the content; then, the sections are thematic. Different applications and implementations are included; in this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and is useful in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the book is useful for research from the evolutionary computation, artificial intelligence, and image processing communities.

Meta-Heuristics

Author: Ibrahim H. Osman

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 676

ISBN-13: 1461313619

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Meta-heuristics have developed dramatically since their inception in the early 1980s. They have had widespread success in attacking a variety of practical and difficult combinatorial optimization problems. These families of approaches include, but are not limited to greedy random adaptive search procedures, genetic algorithms, problem-space search, neural networks, simulated annealing, tabu search, threshold algorithms, and their hybrids. They incorporate concepts based on biological evolution, intelligent problem solving, mathematical and physical sciences, nervous systems, and statistical mechanics. Since the 1980s, a great deal of effort has been invested in the field of combinatorial optimization theory in which heuristic algorithms have become an important area of research and applications. This volume is drawn from the first conference on Meta-Heuristics and contains 41 papers on the state-of-the-art in heuristic theory and applications. The book treats the following meta-heuristics and applications: Genetic Algorithms, Simulated Annealing, Tabu Search, Networks & Graphs, Scheduling and Control, TSP, and Vehicle Routing Problems. It represents research from the fields of Operations Research, Management Science, Artificial Intelligence and Computer Science.

Book Synopsis Meta-Heuristics by : Ibrahim H. Osman

Download or read book Meta-Heuristics written by Ibrahim H. Osman and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 676 pages. Available in PDF, EPUB and Kindle. Book excerpt: Meta-heuristics have developed dramatically since their inception in the early 1980s. They have had widespread success in attacking a variety of practical and difficult combinatorial optimization problems. These families of approaches include, but are not limited to greedy random adaptive search procedures, genetic algorithms, problem-space search, neural networks, simulated annealing, tabu search, threshold algorithms, and their hybrids. They incorporate concepts based on biological evolution, intelligent problem solving, mathematical and physical sciences, nervous systems, and statistical mechanics. Since the 1980s, a great deal of effort has been invested in the field of combinatorial optimization theory in which heuristic algorithms have become an important area of research and applications. This volume is drawn from the first conference on Meta-Heuristics and contains 41 papers on the state-of-the-art in heuristic theory and applications. The book treats the following meta-heuristics and applications: Genetic Algorithms, Simulated Annealing, Tabu Search, Networks & Graphs, Scheduling and Control, TSP, and Vehicle Routing Problems. It represents research from the fields of Operations Research, Management Science, Artificial Intelligence and Computer Science.

Evolutionary Computing and Artificial Intelligence

Author: Fernando Koch

Publisher: Springer

Published: 2019-03-13

Total Pages: 131

ISBN-13: 9811369364

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This Festschrift volume is published in honor of Takao Terano on the occasion of his retirement. Takao Terano is a leading expert in the areas of agent-based modelling, knowledge systems, evolutionary computation, and service science.The contributions in this volume reflect the breadth and impact of his work. The volume contains 12 full papers related to Takao Terano’s research. They deal with various aspects of artificial intelligence, multi-agent systems, collaborative and social computing, social networks, ubiquitous computing.

Book Synopsis Evolutionary Computing and Artificial Intelligence by : Fernando Koch

Download or read book Evolutionary Computing and Artificial Intelligence written by Fernando Koch and published by Springer. This book was released on 2019-03-13 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Festschrift volume is published in honor of Takao Terano on the occasion of his retirement. Takao Terano is a leading expert in the areas of agent-based modelling, knowledge systems, evolutionary computation, and service science.The contributions in this volume reflect the breadth and impact of his work. The volume contains 12 full papers related to Takao Terano’s research. They deal with various aspects of artificial intelligence, multi-agent systems, collaborative and social computing, social networks, ubiquitous computing.