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Proceedings Of The Seventh International Conference On Genetic Algorithms
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Book Synopsis Proceedings of the Seventh International Conference on Genetic Algorithms by : Thomas Bäck
Download or read book Proceedings of the Seventh International Conference on Genetic Algorithms written by Thomas Bäck and published by Morgan Kaufmann Publishers. This book was released on 1997 with total page 832 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proceedings of the biennial International Conference on Genetic Algorithms, available for 1989-present.
Book Synopsis Proceedings of the Seventh International Conference on Mathematics and Computing by : Debasis Giri
Download or read book Proceedings of the Seventh International Conference on Mathematics and Computing written by Debasis Giri and published by Springer Nature. This book was released on 2022-03-05 with total page 1109 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features selected papers from the 7th International Conference on Mathematics and Computing (ICMC 2021), organized by Indian Institute of Engineering Science and Technology (IIEST), Shibpur, India, during March 2021. It covers recent advances in the field of mathematics, statistics, and scientific computing. The book presents innovative work by leading academics, researchers, and experts from industry.
Book Synopsis Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012) by : Jagdish C. Bansal
Download or read book Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012) written by Jagdish C. Bansal and published by Springer Science & Business Media. This book was released on 2012-12-04 with total page 559 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is a collection of high quality peer reviewed research papers presented in Seventh International Conference on Bio-Inspired Computing (BIC-TA 2012) held at ABV-IIITM Gwalior, India. These research papers provide the latest developments in the broad area of "Computational Intelligence". The book discusses wide variety of industrial, engineering and scientific applications of nature/bio-inspired computing and presents invited papers from the inventors/originators of novel computational techniques.
Book Synopsis Genetic Algorithms in Electromagnetics by : Randy L. Haupt
Download or read book Genetic Algorithms in Electromagnetics written by Randy L. Haupt and published by John Wiley & Sons. This book was released on 2007-04-27 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: A thorough and insightful introduction to using genetic algorithms to optimize electromagnetic systems Genetic Algorithms in Electromagnetics focuses on optimizing the objective function when a computer algorithm, analytical model, or experimental result describes the performance of an electromagnetic system. It offers expert guidance to optimizing electromagnetic systems using genetic algorithms (GA), which have proven to be tenacious in finding optimal results where traditional techniques fail. Genetic Algorithms in Electromagnetics begins with an introduction to optimization and several commonly used numerical optimization routines, and goes on to feature: Introductions to GA in both binary and continuous variable forms, complete with examples of MATLAB(r) commands Two step-by-step examples of optimizing antenna arrays as well as a comprehensive overview of applications of GA to antenna array design problems Coverage of GA as an adaptive algorithm, including adaptive and smart arrays as well as adaptive reflectors and crossed dipoles Explanations of the optimization of several different wire antennas, starting with the famous "crooked monopole" How to optimize horn, reflector, and microstrip patch antennas, which require significantly more computing power than wire antennas Coverage of GA optimization of scattering, including scattering from frequency selective surfaces and electromagnetic band gap materials Ideas on operator and parameter selection for a GA Detailed explanations of particle swarm optimization and multiple objective optimization An appendix of MATLAB code for experimentation
Book Synopsis Evolutionary Programming VII by : V.W. Porto
Download or read book Evolutionary Programming VII written by V.W. Porto and published by Springer. This book was released on 1998-08-05 with total page 840 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of the 7th International Conference on Evolutionary Programming, EP98, held in San Diego, CA, USA, in March 1998. The volume presents 81 revised full papers selected from an overwhelming number of submissions. The papers are organized in topical sections on economics, emergence and complex systems; issues and innovations in evolutionary computation; applications; evolution-based approaches to engineering design; examining representations and operators; evolutionary computation theory; evolutionary computation and biological modeling; particle swarm; and combinations of evolutionary and neural computation.
Book Synopsis Proceedings of the 7th International Conference on Discrete Element Methods by : Xikui Li
Download or read book Proceedings of the 7th International Conference on Discrete Element Methods written by Xikui Li and published by Springer. This book was released on 2016-12-01 with total page 1414 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the latest advances in Discrete Element Methods (DEM) and technology. It is the proceeding of 7th International Conference on DEM which was held at Dalian University of Technology on August 1 - 4, 2016. The subject of this book are the DEM and related computational techniques such as DDA, FEM/DEM, molecular dynamics, SPH, Meshless methods, etc., which are the main computational methods for modeling discontinua. In comparison to continua which have been already studied for a long time, the research of discontinua is relatively new, but increases dramatically in recent years and has already become an important field. This book will benefit researchers and scientists from the academic fields of physics, engineering and applied mathematics, as well as from industry and national laboratories who are interested in the DEM.
Book Synopsis Evolutionary Algorithms for Solving Multi-Objective Problems by : Carlos Coello Coello
Download or read book Evolutionary Algorithms for Solving Multi-Objective Problems written by Carlos Coello Coello and published by Springer Science & Business Media. This book was released on 2007-08-26 with total page 810 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. It contains exhaustive appendices, index and bibliography and links to a complete set of teaching tutorials, exercises and solutions.
Book Synopsis Genetic Programming and Data Structures by : W.B. Langdon
Download or read book Genetic Programming and Data Structures written by W.B. Langdon and published by Springer Science & Business Media. This book was released on 1998-04-30 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computers that `program themselves' has long been an aim of computer scientists. Recently genetic programming (GP) has started to show its promise by automatically evolving programs. Indeed in a small number of problems GP has evolved programs whose performance is similar to or even slightly better than that of programs written by people. The main thrust of GP has been to automatically create functions. While these can be of great use they contain no memory and relatively little work has addressed automatic creation of program code including stored data. This issue is the main focus of Genetic Programming, and Data Structures: Genetic Programming + Data Structures = Automatic Programming!. This book is motivated by the observation from software engineering that data abstraction (e.g., via abstract data types) is essential in programs created by human programmers. This book shows that abstract data types can be similarly beneficial to the automatic production of programs using GP. Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! shows how abstract data types (stacks, queues and lists) can be evolved using genetic programming, demonstrates how GP can evolve general programs which solve the nested brackets problem, recognises a Dyck context free language, and implements a simple four function calculator. In these cases, an appropriate data structure is beneficial compared to simple indexed memory. This book also includes a survey of GP, with a critical review of experiments with evolving memory, and reports investigations of real world electrical network maintenance scheduling problems that demonstrate that Genetic Algorithms can find low cost viable solutions to such problems. Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! should be of direct interest to computer scientists doing research on genetic programming, genetic algorithms, data structures, and artificial intelligence. In addition, this book will be of interest to practitioners working in all of these areas and to those interested in automatic programming.
Book Synopsis Evolutionary Multi-objective Optimization in Uncertain Environments by : Chi-Keong Goh
Download or read book Evolutionary Multi-objective Optimization in Uncertain Environments written by Chi-Keong Goh and published by Springer Science & Business Media. This book was released on 2009-03-09 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary algorithms are sophisticated search methods that have been found to be very efficient and effective in solving complex real-world multi-objective problems where conventional optimization tools fail to work well. Despite the tremendous amount of work done in the development of these algorithms in the past decade, many researchers assume that the optimization problems are deterministic and uncertainties are rarely examined. The primary motivation of this book is to provide a comprehensive introduction on the design and application of evolutionary algorithms for multi-objective optimization in the presence of uncertainties. In this book, we hope to expose the readers to a range of optimization issues and concepts, and to encourage a greater degree of appreciation of evolutionary computation techniques and the exploration of new ideas that can better handle uncertainties. "Evolutionary Multi-Objective Optimization in Uncertain Environments: Issues and Algorithms" is intended for a wide readership and will be a valuable reference for engineers, researchers, senior undergraduates and graduate students who are interested in the areas of evolutionary multi-objective optimization and uncertainties.
Book Synopsis Genetic Algorithms and Fuzzy Multiobjective Optimization by : Masatoshi Sakawa
Download or read book Genetic Algorithms and Fuzzy Multiobjective Optimization written by Masatoshi Sakawa and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the introduction of genetic algorithms in the 1970s, an enormous number of articles together with several significant monographs and books have been published on this methodology. As a result, genetic algorithms have made a major contribution to optimization, adaptation, and learning in a wide variety of unexpected fields. Over the years, many excellent books in genetic algorithm optimization have been published; however, they focus mainly on single-objective discrete or other hard optimization problems under certainty. There appears to be no book that is designed to present genetic algorithms for solving not only single-objective but also fuzzy and multiobjective optimization problems in a unified way. Genetic Algorithms And Fuzzy Multiobjective Optimization introduces the latest advances in the field of genetic algorithm optimization for 0-1 programming, integer programming, nonconvex programming, and job-shop scheduling problems under multiobjectiveness and fuzziness. In addition, the book treats a wide range of actual real world applications. The theoretical material and applications place special stress on interactive decision-making aspects of fuzzy multiobjective optimization for human-centered systems in most realistic situations when dealing with fuzziness. The intended readers of this book are senior undergraduate students, graduate students, researchers, and practitioners in the fields of operations research, computer science, industrial engineering, management science, systems engineering, and other engineering disciplines that deal with the subjects of multiobjective programming for discrete or other hard optimization problems under fuzziness. Real world research applications are used throughout the book to illustrate the presentation. These applications are drawn from complex problems. Examples include flexible scheduling in a machine center, operation planning of district heating and cooling plants, and coal purchase planning in an actual electric power plant.
Book Synopsis Evolutionary Intelligence by : S. Sumathi
Download or read book Evolutionary Intelligence written by S. Sumathi and published by Springer Science & Business Media. This book was released on 2008-05-15 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a highly accessible introduction to evolutionary computation. It details basic concepts, highlights several applications of evolutionary computation, and includes solved problems using MATLAB software and C/C++. This book also outlines some ideas on when genetic algorithms and genetic programming should be used. The most difficult part of using a genetic algorithm is how to encode the population, and the author discusses various ways to do this.
Book Synopsis Evolutionary and Adaptive Computing in Engineering Design by : Ian C. Parmee
Download or read book Evolutionary and Adaptive Computing in Engineering Design written by Ian C. Parmee and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Following an introduction to the various techniques and examples of their routine application, this potential is explored through the introduction of various strategies that support searches across a far broader set of possible design solutions within time and budget constraints. Generic problem areas investigated include: - design decomposition; - whole-system design; - multi-objective and constraint satisfaction; - human-computer interaction; - computational expense. Appropriate strategies that help overcome problems often encountered when integrating computer-based techniques with complex, real-world design environments are described. A straightforward approach coupled with examples supports a rapid understanding of the manner in which such strategies can best be designed to handle the complexities of a particular problem.
Book Synopsis Data Mining: A Heuristic Approach by : Abbass, Hussein A.
Download or read book Data Mining: A Heuristic Approach written by Abbass, Hussein A. and published by IGI Global. This book was released on 2001-07-01 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Real life problems are known to be messy, dynamic and multi-objective, and involve high levels of uncertainty and constraints. Because traditional problem-solving methods are no longer capable of handling this level of complexity, heuristic search methods have attracted increasing attention in recent years for solving such problems. Inspired by nature, biology, statistical mechanics, physics and neuroscience, heuristics techniques are used to solve many problems where traditional methods have failed. Data Mining: A Heuristic Approach will be a repository for the applications of these techniques in the area of data mining.
Book Synopsis Adaptive Computing in Design and Manufacture V by : I.C. Parmee
Download or read book Adaptive Computing in Design and Manufacture V written by I.C. Parmee and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Adaptive Computing in Design and Manufacture Conference series is now in its tenth year and has become a well-established, application-oriented meeting recognised by several UK Engineering Institutions and the International Society of Genetic and Evolutionary Computing. The main theme of the conference again relates to the integration of evolutionary and adaptive computing technologies with design and manufacturing processes whilst also taking into account complementary advanced computing technologies. Evolutionary and adaptive computing techniques continue to increase their penetration of industrial and commercial practice as their powerful search, exploration and optimisation capabilities become ever more apparent. The last two years have seen a very significant increase in the development of commercial software tools utilising adaptive computing technologies and the emergence of related commercial research and consultancy organisations supporting the introduction of best practice in terms of industrial utilisation. Adaptive Computing in Design and Manufacture V is comprised of selected papers that cover a diverse set of industrial application areas including: engineering design and design environments, manufacturing process design, scheduling and control, electronic circuit design, fault detection. Various aspects of search and optimisation such as multi-objective and constrained optimisation are also investigated in the context of integration with industrial processes. In addition to evolutionary computing techniques, both neural-net and agent-based technologies play a role in a number of contributions. This collection of papers will be of particular interest to both industrial researchers and practitioners in addition to the academic research communities of engineering, operational research and computer science.
Author :Carlos A. Coello Coello Publisher :Springer Science & Business Media ISBN 13 :9780306467622 Total Pages :616 pages Book Rating :4.4/5 (676 download)
Book Synopsis Evolutionary Algorithms for Solving Multi-Objective Problems by : Carlos A. Coello Coello
Download or read book Evolutionary Algorithms for Solving Multi-Objective Problems written by Carlos A. Coello Coello and published by Springer Science & Business Media. This book was released on 2002 with total page 616 pages. Available in PDF, EPUB and Kindle. Book excerpt: The solving of multi-objective problems (MOPs) has been a continuing effort by humans in many diverse areas, including computer science, engineering, economics, finance, industry, physics, chemistry, and ecology, among others. Many powerful and deterministic and stochastic techniques for solving these large dimensional optimization problems have risen out of operations research, decision science, engineering, computer science and other related disciplines. The explosion in computing power continues to arouse extraordinary interest in stochastic search algorithms that require high computational speed and very large memories. A generic stochastic approach is that of evolutionary algorithms (EA). Such algorithms have been demonstrated to be very powerful and generally applicable for solving different single objective problems. Their fundamental algorithmic structures can also be applied to solving many multi-objective problems. In this book, the various features of multi-objective evolutionary algorithms (MOEAs) are presented in an innovative and unique fashion, with detailed customized forms suggested for a variety of applications. Also, extensive MOEA discussion questions and possible research directions are presented at the end of each chapter. For additional information and supplementary teaching materials, please visit the authors' website at http://www.cs.cinvestav.mx/~EVOCINV/bookinfo.html.
Book Synopsis Multi-Objective Optimization using Evolutionary Algorithms by : Kalyanmoy Deb
Download or read book Multi-Objective Optimization using Evolutionary Algorithms written by Kalyanmoy Deb and published by John Wiley & Sons. This book was released on 2001-07-05 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimierung mit mehreren Zielen, evolutionäre Algorithmen: Dieses Buch wendet sich vorrangig an Einsteiger, denn es werden kaum Vorkenntnisse vorausgesetzt. Geboten werden alle notwendigen Grundlagen, um die Theorie auf Probleme der Ingenieurtechnik, der Vorhersage und der Planung anzuwenden. Der Autor gibt auch einen Ausblick auf Forschungsaufgaben der Zukunft.
Book Synopsis Genetic Algorithms for Machine Learning by : John J. Grefenstette
Download or read book Genetic Algorithms for Machine Learning written by John J. Grefenstette and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: The articles presented here were selected from preliminary versions presented at the International Conference on Genetic Algorithms in June 1991, as well as at a special Workshop on Genetic Algorithms for Machine Learning at the same Conference. Genetic algorithms are general-purpose search algorithms that use principles inspired by natural population genetics to evolve solutions to problems. The basic idea is to maintain a population of knowledge structure that represent candidate solutions to the problem of interest. The population evolves over time through a process of competition (i.e. survival of the fittest) and controlled variation (i.e. recombination and mutation). Genetic Algorithms for Machine Learning contains articles on three topics that have not been the focus of many previous articles on GAs, namely concept learning from examples, reinforcement learning for control, and theoretical analysis of GAs. It is hoped that this sample will serve to broaden the acquaintance of the general machine learning community with the major areas of work on GAs. The articles in this book address a number of central issues in applying GAs to machine learning problems. For example, the choice of appropriate representation and the corresponding set of genetic learning operators is an important set of decisions facing a user of a genetic algorithm. The study of genetic algorithms is proceeding at a robust pace. If experimental progress and theoretical understanding continue to evolve as expected, genetic algorithms will continue to provide a distinctive approach to machine learning. Genetic Algorithms for Machine Learning is an edited volume of original research made up of invited contributions by leading researchers.
