Evolutionary Optimization

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Evolutionary Optimization Algorithms

Author : Dan Simon
Publisher : John Wiley & Sons
ISBN 13 : 1118659503
Total Pages : 776 pages
Book Rating : 4.1/5 (186 download)

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Book Synopsis Evolutionary Optimization Algorithms by : Dan Simon

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.

Evolutionary Optimization

Author : Ruhul Sarker
Publisher : Springer Science & Business Media
ISBN 13 : 0792376544
Total Pages : 416 pages
Book Rating : 4.7/5 (923 download)

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Book Synopsis Evolutionary Optimization by : Ruhul Sarker

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.

Evolutionary Optimization in Dynamic Environments

Author : Jürgen Branke
Publisher : Springer Science & Business Media
ISBN 13 : 1461509114
Total Pages : 217 pages
Book Rating : 4.4/5 (615 download)

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Book Synopsis Evolutionary Optimization in Dynamic Environments by : Jürgen Branke

Download or read book Evolutionary Optimization in Dynamic Environments written by Jürgen Branke and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary Algorithms (EAs) have grown into a mature field of research in optimization, and have proven to be effective and robust problem solvers for a broad range of static real-world optimization problems. Yet, since they are based on the principles of natural evolution, and since natural evolution is a dynamic process in a changing environment, EAs are also well suited to dynamic optimization problems. Evolutionary Optimization in Dynamic Environments is the first comprehensive work on the application of EAs to dynamic optimization problems. It provides an extensive survey on research in the area and shows how EAs can be successfully used to continuously and efficiently adapt a solution to a changing environment, find a good trade-off between solution quality and adaptation cost, find robust solutions whose quality is insensitive to changes in the environment, find flexible solutions which are not only good but that can be easily adapted when necessary. All four aspects are treated in this book, providing a holistic view on the challenges and opportunities when applying EAs to dynamic optimization problems. The comprehensive and up-to-date coverage of the subject, together with details of latest original research, makes Evolutionary Optimization in Dynamic Environments an invaluable resource for researchers and professionals who are dealing with dynamic and stochastic optimization problems, and who are interested in applying local search heuristics, such as evolutionary algorithms.

Data-Driven Evolutionary Optimization

Author : Yaochu Jin
Publisher : Springer Nature
ISBN 13 : 3030746402
Total Pages : 393 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis Data-Driven Evolutionary Optimization by : Yaochu Jin

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.

Evolutionary Optimization Algorithms

Author : Altaf Q. H. Badar
Publisher : CRC Press
ISBN 13 : 1000462145
Total Pages : 274 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Evolutionary Optimization Algorithms by : Altaf Q. H. Badar

Download or read book Evolutionary Optimization Algorithms written by Altaf Q. H. Badar and published by CRC Press. This book was released on 2021-10-29 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive reference text discusses evolutionary optimization techniques, to find optimal solutions for single and multi-objective problems. The text presents each evolutionary optimization algorithm along with its history and other working equations. It also discusses variants and hybrids of optimization techniques. The text presents step-by-step solution to a problem and includes software’s like MATLAB and Python for solving optimization problems. It covers important optimization algorithms including single objective optimization, multi objective optimization, Heuristic optimization techniques, shuffled frog leaping algorithm, bacteria foraging algorithm and firefly algorithm. Aimed at senior undergraduate and graduate students in the field of electrical engineering, electronics engineering, mechanical engineering, and computer science and engineering, this text: Provides step-by-step solution for each evolutionary optimization algorithm. Provides flowcharts and graphics for better understanding of optimization techniques. Discusses popular optimization techniques include particle swarm optimization and genetic algorithm. Presents every optimization technique along with the history and working equations. Includes latest software like Python and MATLAB.

Evolutionary Multiobjective Optimization

Author : Ajith Abraham
Publisher : Springer Science & Business Media
ISBN 13 : 1846281377
Total Pages : 313 pages
Book Rating : 4.8/5 (462 download)

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Book Synopsis Evolutionary Multiobjective Optimization by : Ajith Abraham

Download or read book Evolutionary Multiobjective Optimization written by Ajith Abraham and published by Springer Science & Business Media. This book was released on 2005-09-05 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary Multi-Objective Optimization is an expanding field of research. This book brings a collection of papers with some of the most recent advances in this field. The topic and content is currently very fashionable and has immense potential for practical applications and includes contributions from leading researchers in the field. Assembled in a compelling and well-organised fashion, Evolutionary Computation Based Multi-Criteria Optimization will prove beneficial for both academic and industrial scientists and engineers engaged in research and development and application of evolutionary algorithm based MCO. Packed with must-find information, this book is the first to comprehensively and clearly address the issue of evolutionary computation based MCO, and is an essential read for any researcher or practitioner of the technique.

Evolutionary Algorithms for Solving Multi-Objective Problems

Author : Carlos Coello Coello
Publisher : Springer Science & Business Media
ISBN 13 : 0387367977
Total Pages : 810 pages
Book Rating : 4.3/5 (873 download)

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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.

Evolutionary Computation for Modeling and Optimization

Author : Daniel Ashlock
Publisher : Springer Science & Business Media
ISBN 13 : 0387319093
Total Pages : 578 pages
Book Rating : 4.3/5 (873 download)

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Book Synopsis Evolutionary Computation for Modeling and Optimization by : Daniel Ashlock

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.

Multimodal Optimization by Means of Evolutionary Algorithms

Author : Mike Preuss
Publisher : Springer
ISBN 13 : 3319074075
Total Pages : 206 pages
Book Rating : 4.3/5 (19 download)

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Book Synopsis Multimodal Optimization by Means of Evolutionary Algorithms by : Mike Preuss

Download or read book Multimodal Optimization by Means of Evolutionary Algorithms written by Mike Preuss and published by Springer. This book was released on 2015-11-27 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers the first comprehensive taxonomy for multimodal optimization algorithms, work with its root in topics such as niching, parallel evolutionary algorithms, and global optimization. The author explains niching in evolutionary algorithms and its benefits; he examines their suitability for use as diagnostic tools for experimental analysis, especially for detecting problem (type) properties; and he measures and compares the performances of niching and canonical EAs using different benchmark test problem sets. His work consolidates the recent successes in this domain, presenting and explaining use cases, algorithms, and performance measures, with a focus throughout on the goals of the optimization processes and a deep understanding of the algorithms used. The book will be useful for researchers and practitioners in the area of computational intelligence, particularly those engaged with heuristic search, multimodal optimization, evolutionary computing, and experimental analysis.

Meta-heuristic and Evolutionary Algorithms for Engineering Optimization

Author : Omid Bozorg-Haddad
Publisher : John Wiley & Sons
ISBN 13 : 1119386993
Total Pages : 306 pages
Book Rating : 4.1/5 (193 download)

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Book Synopsis Meta-heuristic and Evolutionary Algorithms for Engineering Optimization by : Omid Bozorg-Haddad

Download or read book Meta-heuristic and Evolutionary Algorithms for Engineering Optimization written by Omid Bozorg-Haddad and published by John Wiley & Sons. This book was released on 2017-10-09 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: A detailed review of a wide range of meta-heuristic and evolutionary algorithms in a systematic manner and how they relate to engineering optimization problems This book introduces the main metaheuristic algorithms and their applications in optimization. It describes 20 leading meta-heuristic and evolutionary algorithms and presents discussions and assessments of their performance in solving optimization problems from several fields of engineering. The book features clear and concise principles and presents detailed descriptions of leading methods such as the pattern search (PS) algorithm, the genetic algorithm (GA), the simulated annealing (SA) algorithm, the Tabu search (TS) algorithm, the ant colony optimization (ACO), and the particle swarm optimization (PSO) technique. Chapter 1 of Meta-heuristic and Evolutionary Algorithms for Engineering Optimization provides an overview of optimization and defines it by presenting examples of optimization problems in different engineering domains. Chapter 2 presents an introduction to meta-heuristic and evolutionary algorithms and links them to engineering problems. Chapters 3 to 22 are each devoted to a separate algorithm— and they each start with a brief literature review of the development of the algorithm, and its applications to engineering problems. The principles, steps, and execution of the algorithms are described in detail, and a pseudo code of the algorithm is presented, which serves as a guideline for coding the algorithm to solve specific applications. This book: Introduces state-of-the-art metaheuristic algorithms and their applications to engineering optimization; Fills a gap in the current literature by compiling and explaining the various meta-heuristic and evolutionary algorithms in a clear and systematic manner; Provides a step-by-step presentation of each algorithm and guidelines for practical implementation and coding of algorithms; Discusses and assesses the performance of metaheuristic algorithms in multiple problems from many fields of engineering; Relates optimization algorithms to engineering problems employing a unifying approach. Meta-heuristic and Evolutionary Algorithms for Engineering Optimization is a reference intended for students, engineers, researchers, and instructors in the fields of industrial engineering, operations research, optimization/mathematics, engineering optimization, and computer science. OMID BOZORG-HADDAD, PhD, is Professor in the Department of Irrigation and Reclamation Engineering at the University of Tehran, Iran. MOHAMMAD SOLGI, M.Sc., is Teacher Assistant for M.Sc. courses at the University of Tehran, Iran. HUGO A. LOÁICIGA, PhD, is Professor in the Department of Geography at the University of California, Santa Barbara, United States of America.

Evolutionary Computation for Dynamic Optimization Problems

Author : Shengxiang Yang
Publisher : Springer
ISBN 13 : 3642384161
Total Pages : 479 pages
Book Rating : 4.6/5 (423 download)

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Book Synopsis Evolutionary Computation for Dynamic Optimization Problems by : Shengxiang Yang

Download or read book Evolutionary Computation for Dynamic Optimization Problems written by Shengxiang Yang and published by Springer. This book was released on 2013-11-18 with total page 479 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a compilation on the state-of-the-art and recent advances of evolutionary computation for dynamic optimization problems. The motivation for this book arises from the fact that many real-world optimization problems and engineering systems are subject to dynamic environments, where changes occur over time. Key issues for addressing dynamic optimization problems in evolutionary computation, including fundamentals, algorithm design, theoretical analysis, and real-world applications, are presented. "Evolutionary Computation for Dynamic Optimization Problems" is a valuable reference to scientists, researchers, professionals and students in the field of engineering and science, particularly in the areas of computational intelligence, nature- and bio-inspired computing, and evolutionary computation.

Optimization Using Evolutionary Algorithms and Metaheuristics

Author : Kaushik Kumar
Publisher : CRC Press
ISBN 13 : 1000546802
Total Pages : 127 pages
Book Rating : 4.0/5 (5 download)

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Book Synopsis Optimization Using Evolutionary Algorithms and Metaheuristics by : Kaushik Kumar

Download or read book Optimization Using Evolutionary Algorithms and Metaheuristics written by Kaushik Kumar and published by CRC Press. This book was released on 2019-08-22 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: Metaheuristic optimization is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity. This is usually applied when two or more objectives are to be optimized simultaneously. This book is presented with two major objectives. Firstly, it features chapters by eminent researchers in the field providing the readers about the current status of the subject. Secondly, algorithm-based optimization or advanced optimization techniques, which are applied to mostly non-engineering problems, are applied to engineering problems. This book will also serve as an aid to both research and industry. Usage of these methodologies would enable the improvement in engineering and manufacturing technology and support an organization in this era of low product life cycle. Features: Covers the application of recent and new algorithms Focuses on the development aspects such as including surrogate modeling, parallelization, game theory, and hybridization Presents the advances of engineering applications for both single-objective and multi-objective optimization problems Offers recent developments from a variety of engineering fields Discusses Optimization using Evolutionary Algorithms and Metaheuristics applications in engineering

Differential Evolution

Author : Kenneth Price
Publisher : Springer Science & Business Media
ISBN 13 : 3540313060
Total Pages : 544 pages
Book Rating : 4.5/5 (43 download)

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Book Synopsis Differential Evolution by : Kenneth Price

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.

Multi-Objective Optimization using Evolutionary Algorithms

Author : Kalyanmoy Deb
Publisher : John Wiley & Sons
ISBN 13 : 9780471873396
Total Pages : 540 pages
Book Rating : 4.8/5 (733 download)

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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.

Theory of Evolutionary Computation

Author : Benjamin Doerr
Publisher : Springer Nature
ISBN 13 : 3030294145
Total Pages : 527 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Theory of Evolutionary Computation by : Benjamin Doerr

Download or read book Theory of Evolutionary Computation written by Benjamin Doerr and published by Springer Nature. This book was released on 2019-11-20 with total page 527 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited book reports on recent developments in the theory of evolutionary computation, or more generally the domain of randomized search heuristics. It starts with two chapters on mathematical methods that are often used in the analysis of randomized search heuristics, followed by three chapters on how to measure the complexity of a search heuristic: black-box complexity, a counterpart of classical complexity theory in black-box optimization; parameterized complexity, aimed at a more fine-grained view of the difficulty of problems; and the fixed-budget perspective, which answers the question of how good a solution will be after investing a certain computational budget. The book then describes theoretical results on three important questions in evolutionary computation: how to profit from changing the parameters during the run of an algorithm; how evolutionary algorithms cope with dynamically changing or stochastic environments; and how population diversity influences performance. Finally, the book looks at three algorithm classes that have only recently become the focus of theoretical work: estimation-of-distribution algorithms; artificial immune systems; and genetic programming. Throughout the book the contributing authors try to develop an understanding for how these methods work, and why they are so successful in many applications. The book will be useful for students and researchers in theoretical computer science and evolutionary computing.

Evolutionary Algorithms and Neural Networks

Author : Seyedali Mirjalili
Publisher : Springer
ISBN 13 : 3319930257
Total Pages : 164 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Evolutionary Algorithms and Neural Networks by : Seyedali Mirjalili

Download or read book Evolutionary Algorithms and Neural Networks written by Seyedali Mirjalili and published by Springer. This book was released on 2018-06-26 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces readers to the fundamentals of artificial neural networks, with a special emphasis on evolutionary algorithms. At first, the book offers a literature review of several well-regarded evolutionary algorithms, including particle swarm and ant colony optimization, genetic algorithms and biogeography-based optimization. It then proposes evolutionary version of several types of neural networks such as feed forward neural networks, radial basis function networks, as well as recurrent neural networks and multi-later perceptron. Most of the challenges that have to be addressed when training artificial neural networks using evolutionary algorithms are discussed in detail. The book also demonstrates the application of the proposed algorithms for several purposes such as classification, clustering, approximation, and prediction problems. It provides a tutorial on how to design, adapt, and evaluate artificial neural networks as well, and includes source codes for most of the proposed techniques as supplementary materials.

Evolutionary Topology Optimization of Continuum Structures

Author : Xiaodong Huang
Publisher : John Wiley & Sons
ISBN 13 : 9780470689479
Total Pages : 240 pages
Book Rating : 4.6/5 (894 download)

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Book Synopsis Evolutionary Topology Optimization of Continuum Structures by : Xiaodong Huang

Download or read book Evolutionary Topology Optimization of Continuum Structures written by Xiaodong Huang and published by John Wiley & Sons. This book was released on 2010-03-11 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary Topology Optimization of Continuum Structures treads new ground with a comprehensive study on the techniques and applications of evolutionary structural optimization (ESO) and its later version bi-directional ESO (BESO) methods. Since the ESO method was first introduced by Xie and Steven in 1992 and the publication of their well-known book Evolutionary Structural Optimization in 1997, there have been significant improvements in the techniques as well as important practical applications. The authors present these developments, illustrated by numerous interesting and detailed examples. They clearly demonstrate that the evolutionary structural optimization method is an effective approach capable of solving a wide range of topology optimization problems, including structures with geometrical and material nonlinearities, energy absorbing devices, periodical structures, bridges and buildings. Presents latest developments and applications in this increasingly popular & maturing optimization approach for engineers and architects; Authored by leading researchers in the field who have been working in the area of ESO and BESO developments since their conception; Includes a number of test problems for students as well as a chapter of case studies that includes several recent practical projects in which the authors have been involved; Accompanied by a website housing ESO/BESO computer programs at http://www.wiley.com/go/huang and test examples, as well as a chapter within the book giving a description and step-by-step instruction on how to use the software package BESO2D. Evolutionary Topology Optimization of Continuum Structures will appeal to researchers and graduate students working in structural design and optimization, and will also be of interest to civil and structural engineers, architects and mechanical engineers involved in creating innovative and efficient structures.