Download or read book Numerical and Evolutionary Optimization 2018 written by Adriana Lara and published by MDPI. This book was released on 2019-11-19 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book was established after the 6th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications.
Download or read book Differential Evolution written by Kenneth Price and published by Springer Science & Business Media. This book was released on 2006-03-04 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Problems demanding globally optimal solutions are ubiquitous, yet many are intractable when they involve constrained functions having many local optima and interacting, mixed-type variables. The differential evolution (DE) algorithm is a practical approach to global numerical optimization which is easy to understand, simple to implement, reliable, and fast. Packed with illustrations, computer code, new insights, and practical advice, this volume explores DE in both principle and practice. It is a valuable resource for professionals needing a proven optimizer and for students wanting an evolutionary perspective on global numerical optimization.
Download or read book Data Driven Evolutionary Optimization written by Yaochu Jin and published by Springer Nature. This book was released on 2021-06-28 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intended for researchers and practitioners alike, this book covers carefully selected yet broad topics in optimization, machine learning, and metaheuristics. Written by world-leading academic researchers who are extremely experienced in industrial applications, this self-contained book is the first of its kind that provides comprehensive background knowledge, particularly practical guidelines, and state-of-the-art techniques. New algorithms are carefully explained, further elaborated with pseudocode or flowcharts, and full working source code is made freely available. This is followed by a presentation of a variety of data-driven single- and multi-objective optimization algorithms that seamlessly integrate modern machine learning such as deep learning and transfer learning with evolutionary and swarm optimization algorithms. Applications of data-driven optimization ranging from aerodynamic design, optimization of industrial processes, to deep neural architecture search are included.
Download or read book Hybrid Evolutionary Algorithms written by Crina Grosan and published by Springer. This book was released on 2007-08-29 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited volume is targeted at presenting the latest state-of-the-art methodologies in "Hybrid Evolutionary Algorithms". The chapters deal with the theoretical and methodological aspects, as well as various applications to many real world problems from science, technology, business or commerce. Overall, the book has 14 chapters including an introductory chapter giving the fundamental definitions and some important research challenges. The contributions were selected on the basis of fundamental ideas/concepts rather than the thoroughness of techniques deployed.
Download or read book Numerical and Evolutionary Optimization NEO 2017 written by Leonardo Trujillo and published by Springer. This book was released on 2018-07-12 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features 15 chapters based on the Numerical and Evolutionary Optimization (NEO 2017) workshop, held from September 27 to 29 in the city of Tijuana, Mexico. The event gathered researchers from two complimentary fields to discuss the theory, development and application of state-of-the-art techniques to address search and optimization problems. The lively event included 7 invited talks and 64 regular talks covering a wide range of topics, from evolutionary computer vision and machine learning with evolutionary computation, to set oriented numeric and steepest descent techniques. Including research submitted by the NEO community, the book provides informative and stimulating material for future research in the field.
Download or read book Evolutionary Optimization Algorithms written by Dan Simon and published by John Wiley & Sons. This book was released on 2013-06-13 with total page 776 pages. Available in PDF, EPUB and Kindle. Book excerpt: A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies. This book discusses the theory, history, mathematics, and programming of evolutionary optimization algorithms. Featured algorithms include genetic algorithms, genetic programming, ant colony optimization, particle swarm optimization, differential evolution, biogeography-based optimization, and many others. Evolutionary Optimization Algorithms: Provides a straightforward, bottom-up approach that assists the reader in obtaining a clear but theoretically rigorous understanding of evolutionary algorithms, with an emphasis on implementation Gives a careful treatment of recently developed EAs including opposition-based learning, artificial fish swarms, bacterial foraging, and many others and discusses their similarities and differences from more well-established EAs Includes chapter-end problems plus a solutions manual available online for instructors Offers simple examples that provide the reader with an intuitive understanding of the theory Features source code for the examples available on the author's website Provides advanced mathematical techniques for analyzing EAs, including Markov modeling and dynamic system modeling Evolutionary Optimization Algorithms: Biologically Inspired and Population-Based Approaches to Computer Intelligence is an ideal text for advanced undergraduate students, graduate students, and professionals involved in engineering and computer science.
Download or read book Noisy Optimization With Evolution Strategies written by Dirk V. Arnold and published by Springer. This book was released on 2012-10-24 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Noise is a common factor in most real-world optimization problems. Sources of noise can include physical measurement limitations, stochastic simulation models, incomplete sampling of large spaces, and human-computer interaction. Evolutionary algorithms are general, nature-inspired heuristics for numerical search and optimization that are frequently observed to be particularly robust with regard to the effects of noise. Noisy Optimization with Evolution Strategies contributes to the understanding of evolutionary optimization in the presence of noise by investigating the performance of evolution strategies, a type of evolutionary algorithm frequently employed for solving real-valued optimization problems. By considering simple noisy environments, results are obtained that describe how the performance of the strategies scales with both parameters of the problem and of the strategies considered. Such scaling laws allow for comparisons of different strategy variants, for tuning evolution strategies for maximum performance, and they offer insights and an understanding of the behavior of the strategies that go beyond what can be learned from mere experimentation. This first comprehensive work on noisy optimization with evolution strategies investigates the effects of systematic fitness overvaluation, the benefits of distributed populations, and the potential of genetic repair for optimization in the presence of noise. The relative robustness of evolution strategies is confirmed in a comparison with other direct search algorithms. Noisy Optimization with Evolution Strategies is an invaluable resource for researchers and practitioners of evolutionary algorithms.
Download or read book Evolutionary Optimization written by Ruhul Sarker and published by Springer Science & Business Media. This book was released on 2002-01-31 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of evolutionary computation techniques has grown considerably over the past several years. Over this time, the use and applications of these techniques have been further enhanced resulting in a set of computational intelligence (also known as modern heuristics) tools that are particularly adept for solving complex optimization problems. Moreover, they are characteristically more robust than traditional methods based on formal logics or mathematical programming for many real world OR/MS problems. Hence, evolutionary computation techniques have dealt with complex optimization problems better than traditional optimization techniques although they can be applied to easy and simple problems where conventional techniques work well. Clearly there is a need for a volume that both reviews state-of-the-art evolutionary computation techniques, and surveys the most recent developments in their use for solving complex OR/MS problems. This volume on Evolutionary Optimization seeks to fill this need. Evolutionary Optimization is a volume of invited papers written by leading researchers in the field. All papers were peer reviewed by at least two recognized reviewers. The book covers the foundation as well as the practical side of evolutionary optimization.
Download or read book Numerical Optimization in Engineering and Sciences written by Debashis Dutta and published by Springer Nature. This book was released on 2020-04-07 with total page 569 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents select peer-reviewed papers presented at the International Conference on Numerical Optimization in Engineering and Sciences (NOIEAS) 2019. The book covers a wide variety of numerical optimization techniques across all major engineering disciplines like mechanical, manufacturing, civil, electrical, chemical, computer, and electronics engineering. The major focus is on innovative ideas, current methods and latest results involving advanced optimization techniques. The contents provide a good balance between numerical models and analytical results obtained for different engineering problems and challenges. This book will be useful for students, researchers, and professionals interested in engineering optimization techniques.
Download or read book Knowledge Incorporation in Evolutionary Computation written by Yaochu Jin and published by Springer. This book was released on 2013-04-22 with total page 543 pages. Available in PDF, EPUB and Kindle. Book excerpt: Incorporation of a priori knowledge, such as expert knowledge, meta-heuristics and human preferences, as well as domain knowledge acquired during evolu tionary search, into evolutionary algorithms has received increasing interest in the recent years. It has been shown from various motivations that knowl edge incorporation into evolutionary search is able to significantly improve search efficiency. However, results on knowledge incorporation in evolution ary computation have been scattered in a wide range of research areas and a systematic handling of this important topic in evolutionary computation still lacks. This edited book is a first attempt to put together the state-of-art and re cent advances on knowledge incorporation in evolutionary computation within a unified framework. Existing methods for knowledge incorporation are di vided into the following five categories according to the functionality of the incorporated knowledge in the evolutionary algorithms. 1. Knowledge incorporation in representation, population initialization, - combination and mutation. 2. Knowledge incorporation in selection and reproduction. 3. Knowledge incorporation in fitness evaluations. 4. Knowledge incorporation through life-time learning and human-computer interactions. 5. Incorporation of human preferences in multi-objective evolutionary com putation. The intended readers of this book are graduate students, researchers and practitioners in all fields of science and engineering who are interested in evolutionary computation. The book is divided into six parts. Part I contains one introductory chapter titled "A selected introduction to evolutionary computation" by Yao, which presents a concise but insightful introduction to evolutionary computation.
Download or read book Evolutionary Computation for Modeling and Optimization written by Daniel Ashlock and published by Springer Science & Business Media. This book was released on 2006-04-04 with total page 578 pages. Available in PDF, EPUB and Kindle. Book excerpt: Concentrates on developing intuition about evolutionary computation and problem solving skills and tool sets. Lots of applications and test problems, including a biotechnology chapter.
Download or read book Evolutionary Optimization written by Ruhul Sarker and published by Springer Science & Business Media. This book was released on 2006-04-11 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary computation techniques have attracted increasing att- tions in recent years for solving complex optimization problems. They are more robust than traditional methods based on formal logics or mathematical programming for many real world OR/MS problems. E- lutionary computation techniques can deal with complex optimization problems better than traditional optimization techniques. However, most papers on the application of evolutionary computation techniques to Operations Research /Management Science (OR/MS) problems have scattered around in different journals and conference proceedings. They also tend to focus on a very special and narrow topic. It is the right time that an archival book series publishes a special volume which - cludes critical reviews of the state-of-art of those evolutionary com- tation techniques which have been found particularly useful for OR/MS problems, and a collection of papers which represent the latest devel- ment in tackling various OR/MS problems by evolutionary computation techniques. This special volume of the book series on Evolutionary - timization aims at filling in this gap in the current literature. The special volume consists of invited papers written by leading - searchers in the field. All papers were peer reviewed by at least two recognised reviewers. The book covers the foundation as well as the practical side of evolutionary optimization.
Download or read book Constraint Handling in Evolutionary Optimization written by Efrén Mezura-Montes and published by Springer Science & Business Media. This book was released on 2009-04-07 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the result of a special session on constraint-handling techniques used in evolutionary algorithms within the Congress on Evolutionary Computation (CEC) in 2007. It presents recent research in constraint-handling in evolutionary optimization.
Download or read book Evolutionary Algorithms in Engineering Applications written by Dipankar Dasgupta and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 561 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary algorithms are general-purpose search procedures based on the mechanisms of natural selection and population genetics. They are appealing because they are simple, easy to interface, and easy to extend. This volume is concerned with applications of evolutionary algorithms and associated strategies in engineering. It will be useful for engineers, designers, developers, and researchers in any scientific discipline interested in the applications of evolutionary algorithms. The volume consists of five parts, each with four or five chapters. The topics are chosen to emphasize application areas in different fields of engineering. Each chapter can be used for self-study or as a reference by practitioners to help them apply evolutionary algorithms to problems in their engineering domains.
Download or read book Genetic Algorithms Data Structures Evolution Programs written by Zbigniew Michalewicz and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic algorithms are founded upon the principle of evolution, i.e., survival of the fittest. Hence evolution programming techniques, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the traveling salesman problem, and problems of scheduling, partitioning, and control. The importance of these techniques is still growing, since evolution programs are parallel in nature, and parallelism is one of the most promising directions in computer science. The book is self-contained and the only prerequisite is basic undergraduate mathematics. This third edition has been substantially revised and extended by three new chapters and by additional appendices containing working material to cover recent developments and a change in the perception of evolutionary computation.
Download or read book Evolution and Optimum Seeking written by Hans-Paul Schwefel and published by Wiley-Interscience. This book was released on 1995-01-23 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents numerical optimization methods and algorithms applied to computer calculations. The methods consist of the adaptation of simple evolutionary rules to a computer procedure which is to search for optimal parameters within a simulation model of a technical device. Accompanied by a diskette containing the algorithms presented in the book.
Download or read book An Introduction to Genetic Algorithms written by Melanie Mitchell and published by MIT Press. This book was released on 1998-03-02 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics—particularly in machine learning, scientific modeling, and artificial life—and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.
