Download or read book Evolutionary Constrained Optimization written by Rituparna Datta and published by Springer. This book was released on 2014-12-13 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book makes available a self-contained collection of modern research addressing the general constrained optimization problems using evolutionary algorithms. Broadly the topics covered include constraint handling for single and multi-objective optimizations; penalty function based methodology; multi-objective based methodology; new constraint handling mechanism; hybrid methodology; scaling issues in constrained optimization; design of scalable test problems; parameter adaptation in constrained optimization; handling of integer, discrete and mix variables in addition to continuous variables; application of constraint handling techniques to real-world problems; and constrained optimization in dynamic environment. There is also a separate chapter on hybrid optimization, which is gaining lots of popularity nowadays due to its capability of bridging the gap between evolutionary and classical optimization. The material in the book is useful to researchers, novice, and experts alike. The book will also be useful for classroom teaching and future research.
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 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 Evolutionary Computations written by Keigo Watanabe and published by Springer. This book was released on 2012-11-02 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary computation, a broad field that includes genetic algorithms, evolution strategies, and evolutionary programming, has proven to offer well-suited techniques for industrial and management tasks - therefore receiving considerable attention from scientists and engineers during the last decade. This monograph develops and analyzes evolutionary algorithms that can be successfully applied to real-world problems such as robotic control. Although of particular interest to robotic control engineers, Evolutionary Computations also may interest the large audience of researchers, engineers, designers and graduate students confronted with complicated optimization tasks.
Download or read book Evolutionary Algorithms for Constrained Optimization Problems written by Jens Gottlieb and published by . This book was released on 1999 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Constraint Handling in Evolutionary Optimization written by Efrén Mezura-Montes and published by Springer. This book was released on 2009-05-03 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 for Constrained Optimization written by Ehab Zaky Mohammed Abdullah Elfeky and published by . This book was released on 2010 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most real world optimization problems, and their corresponding models, are complex. This complexity arises from different sources, such as existence of the constraints, function characteristics, and high dimensionality. Evolutionary Algorithms (EAs) and specially Genetic Algorithms (GAs) have proven themselves as efficient optimization techniques over the last two decades; as they have the ability to overcome the drawbacks of conventional optimization methods. Therefore, this thesis addresses the GAs as a solution methodology for solving Constrained Optimization Problems (COPs). In COPs for practical applications, it is more likely that the optimal solution lies on the feasible region boundary. Utilizing this feature, this thesis introduces a new genetic algorithm for solving small-scale COPs. A new ranking and selection scheme is introduced in conjunction with both a new crossover method based on three parents, and a mixed mutation between two currently existing mutation methodologies. A number of well known benchmark problems have been solved and compared with the state of the art algorithms, and the proposed algorithm shows a competitive and even superior performance for some problems. In addition, a detailed parametric analysis is provided to show the individual effect of each of the proposed components. Furthermore, this thesis introduces another algorithm that breaks down the complexity of the constrained optimization process into smaller dimensions. Every sub-component of the algorithm maintains a part of the problem, and the whole problem optimization is treated through a designed communication process. This algorithm deals with a special problem structure, in which the problems are entirely or almost decomposable based on what is called in the literature as a block angular structure. The proposed algorithm decomposes the constraints as well as the chromosomes. It facilitates solving such problems both with and without overlapping variables between the sub-components. Some experiments have been carried out to show how the designed communication process controls the optimization and what the best parameter settings are. Then, the algorithm has been implemented in a parallel environment on a scalable practical test problem and using this shows how the proposed algorithm outperforms the other single population based algorithms in higher dimensions.
Download or read book Artificial Neural Nets and Genetic Algorithms written by Andrej Dobnikar and published by Springer Science & Business Media. This book was released on 1999-07-15 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the contents: Neural networks – theory and applications: NNs (= neural networks) classifier on continuous data domains– quantum associative memory – a new class of neuron-like discrete filters to image processing – modular NNs for improving generalisation properties – presynaptic inhibition modelling for image processing application – NN recognition system for a curvature primal sketch – NN based nonlinear temporal-spatial noise rejection system – relaxation rate for improving Hopfield network – Oja's NN and influence of the learning gain on its dynamics Genetic algorithms – theory and applications: transposition: a biological-inspired mechanism to use with GAs (= genetic algorithms) – GA for decision tree induction – optimising decision classifications using GAs – scheduling tasks with intertask communication onto multiprocessors by GAs – design of robust networks with GA – effect of degenerate coding on GAs – multiple traffic signal control using a GA – evolving musical harmonisation – niched-penalty approach for constraint handling in GAs – GA with dynamic population size – GA with dynamic niche clustering for multimodal function optimisation Soft computing and uncertainty: self-adaptation of evolutionary constructed decision trees by information spreading – evolutionary programming of near optimal NNs
Download or read book Intelligent Optimization written by Changhe Li and published by Springer Nature. This book was released on with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Introduction to Evolutionary Algorithms written by Xinjie Yu and published by Springer Science & Business Media. This book was released on 2010-06-10 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary algorithms are becoming increasingly attractive across various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science and economics. Introduction to Evolutionary Algorithms presents an insightful, comprehensive, and up-to-date treatment of evolutionary algorithms. It covers such hot topics as: • genetic algorithms, • differential evolution, • swarm intelligence, and • artificial immune systems. The reader is introduced to a range of applications, as Introduction to Evolutionary Algorithms demonstrates how to model real world problems, how to encode and decode individuals, and how to design effective search operators according to the chromosome structures with examples of constraint optimization, multiobjective optimization, combinatorial optimization, and supervised/unsupervised learning. This emphasis on practical applications will benefit all students, whether they choose to continue their academic career or to enter a particular industry. Introduction to Evolutionary Algorithms is intended as a textbook or self-study material for both advanced undergraduates and graduate students. Additional features such as recommended further reading and ideas for research projects combine to form an accessible and interesting pedagogical approach to this widely used discipline.
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.
Download or read book Multiobjective Problem Solving from Nature written by Joshua Knowles and published by Springer Science & Business Media. This book was released on 2008-01-28 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text examines how multiobjective evolutionary algorithms and related techniques can be used to solve problems, particularly in the disciplines of science and engineering. Contributions by leading researchers show how the concept of multiobjective optimization can be used to reformulate and resolve problems in areas such as constrained optimization, co-evolution, classification, inverse modeling, and design.
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 Advances in Artificial Intelligence IBERAMIA 2004 written by Christian Lemaitre and published by Springer. This book was released on 2004-11-03 with total page 1005 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 9th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2004, held in Puebla, Mexico in November 2004. The 97 revised full papers presented were carefully reviewed and selected from 304 submissions. The papers are organized in topical sections on distributed AI and multi-agent systems, knowledge engineering and case-based reasoning, planning and scheduling, machine learning and knowledge acquisition, natural language processing, knowledge representation and reasoning, knowledge discovery and data mining, robotics, computer vision, uncertainty and fuzzy systems, genetic algorithms and neural networks, AI in education, and miscellaneous topics.
Download or read book Applications of Multi objective Evolutionary Algorithms written by Carlos A. Coello Coello and published by World Scientific. This book was released on 2004 with total page 792 pages. Available in PDF, EPUB and Kindle. Book excerpt: - Detailed MOEA applications discussed by international experts - State-of-the-art practical insights in tackling statistical optimization with MOEAs - A unique monograph covering a wide spectrum of real-world applications - Step-by-step discussion of MOEA applications in a variety of domains
Download or read book Self Organizing Migrating Algorithm written by Donald Davendra and published by Springer. This book was released on 2016-02-04 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together the current state of-the-art research in Self Organizing Migrating Algorithm (SOMA) as a novel population-based evolutionary algorithm, modeled on the predator-prey relationship, by its leading practitioners. As the first ever book on SOMA, this book is geared towards graduate students, academics and researchers, who are looking for a good optimization algorithm for their applications. This book presents the methodology of SOMA, covering both the real and discrete domains, and its various implementations in different research areas. The easy-to-follow and implement methodology used in the book will make it easier for a reader to implement, modify and utilize SOMA.
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.
