Advances In Evolutionary Computing

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Advances in Evolutionary Computing

Ashish Ghosh,Shigeyoshi Tsutsui
Advances in Evolutionary Computing Book PDF

Author : Ashish Ghosh,Shigeyoshi Tsutsui
Publisher : Springer Science & Business Media
Page : 1001 pages
File Size : 44,9 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9783642189654

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Advances in Evolutionary Computing by Ashish Ghosh,Shigeyoshi Tsutsui Pdf

This book provides a collection of fourty articles containing new material on both theoretical aspects of Evolutionary Computing (EC), and demonstrating the usefulness/success of it for various kinds of large-scale real world problems. Around 23 articles deal with various theoretical aspects of EC and 17 articles demonstrate the success of EC methodologies. These articles are written by leading experts of the field from different countries all over the world.

Advances in Evolutionary Algorithms

Chang Wook Ahn
Advances in Evolutionary Algorithms Book PDF

Author : Chang Wook Ahn
Publisher : Springer
Page : 172 pages
File Size : 41,9 Mb
Release : 2007-05-22
Category : Technology & Engineering
ISBN : 9783540317593

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Advances in Evolutionary Algorithms by Chang Wook Ahn Pdf

Genetic and evolutionary algorithms (GEAs) have often achieved an enviable success in solving optimization problems in a wide range of disciplines. This book provides effective optimization algorithms for solving a broad class of problems quickly, accurately, and reliably by employing evolutionary mechanisms.

Introduction to Evolutionary Computing

Agoston E. Eiben,J.E. Smith
Introduction to Evolutionary Computing Book PDF

Author : Agoston E. Eiben,J.E. Smith
Publisher : Springer Science & Business Media
Page : 307 pages
File Size : 40,7 Mb
Release : 2013-03-14
Category : Computers
ISBN : 9783662050941

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Introduction to Evolutionary Computing by Agoston E. Eiben,J.E. Smith Pdf

The first complete overview of evolutionary computing, the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. The text is aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. The book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.

Recent Advances in Swarm Intelligence and Evolutionary Computation

Xin-She Yang
Recent Advances in Swarm Intelligence and Evolutionary Computation Book PDF

Author : Xin-She Yang
Publisher : Springer
Page : 295 pages
File Size : 51,7 Mb
Release : 2014-12-27
Category : Technology & Engineering
ISBN : 9783319138268

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Recent Advances in Swarm Intelligence and Evolutionary Computation by Xin-She Yang Pdf

This timely review volume summarizes the state-of-the-art developments in nature-inspired algorithms and applications with the emphasis on swarm intelligence and bio-inspired computation. Topics include the analysis and overview of swarm intelligence and evolutionary computation, hybrid metaheuristic algorithms, bat algorithm, discrete cuckoo search, firefly algorithm, particle swarm optimization, and harmony search as well as convergent hybridization. Application case studies have focused on the dehydration of fruits and vegetables by the firefly algorithm and goal programming, feature selection by the binary flower pollination algorithm, job shop scheduling, single row facility layout optimization, training of feed-forward neural networks, damage and stiffness identification, synthesis of cross-ambiguity functions by the bat algorithm, web document clustering, truss analysis, water distribution networks, sustainable building designs and others. As a timely review, this book can serve as an ideal reference for graduates, lecturers, engineers and researchers in computer science, evolutionary computing, artificial intelligence, machine learning, computational intelligence, data mining, engineering optimization and designs.

Towards a New Evolutionary Computation

Jose A. Lozano,Pedro Larrañaga,Iñaki Inza,Endika Bengoetxea
Towards a New Evolutionary Computation Book PDF

Author : Jose A. Lozano,Pedro Larrañaga,Iñaki Inza,Endika Bengoetxea
Publisher : Springer
Page : 306 pages
File Size : 40,8 Mb
Release : 2006-01-21
Category : Technology & Engineering
ISBN : 9783540324942

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Towards a New Evolutionary Computation by Jose A. Lozano,Pedro Larrañaga,Iñaki Inza,Endika Bengoetxea Pdf

Estimation of Distribution Algorithms (EDAs) are a set of algorithms in the Evolutionary Computation (EC) field characterized by the use of explicit probability distributions in optimization. Contrarily to other EC techniques such as the broadly known Genetic Algorithms (GAs) in EDAs, the crossover and mutation operators are substituted by the sampling of a distribution previously learnt from the selected individuals. EDAs have experienced a high development that has transformed them into an established discipline within the EC field. This book attracts the interest of new researchers in the EC field as well as in other optimization disciplines, and that it becomes a reference for all of us working on this topic. The twelve chapters of this book can be divided into those that endeavor to set a sound theoretical basis for EDAs, those that broaden the methodology of EDAs and finally those that have an applied objective.

Design by Evolution

Philip F. Hingston,Luigi C. Barone,Zbigniew Michalewicz
Design by Evolution Book PDF

Author : Philip F. Hingston,Luigi C. Barone,Zbigniew Michalewicz
Publisher : Springer Science & Business Media
Page : 352 pages
File Size : 49,8 Mb
Release : 2008-09-30
Category : Computers
ISBN : 9783540741114

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Design by Evolution by Philip F. Hingston,Luigi C. Barone,Zbigniew Michalewicz Pdf

Evolution is Nature’s design process. The natural world is full of wonderful examples of its successes, from engineering design feats such as powered flight, to the design of complex optical systems such as the mammalian eye, to the merely stunningly beautiful designs of orchids or birds of paradise. With increasing computational power, we are now able to simulate this process with greater fidelity, combining complex simulations with high-performance evolutionary algorithms to tackle problems that used to be impractical. This book showcases the state of the art in evolutionary algorithms for design. The chapters are organized by experts in the following fields: evolutionary design and "intelligent design" in biology, art, computational embryogeny, and engineering. The book will be of interest to researchers, practitioners and graduate students in natural computing, engineering design, biology and the creative arts.

New Achievements in Evolutionary Computation

Peter Korosec
New Achievements in Evolutionary Computation Book PDF

Author : Peter Korosec
Publisher : BoD – Books on Demand
Page : 330 pages
File Size : 41,7 Mb
Release : 2010-02-01
Category : Computers
ISBN : 9789533070537

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New Achievements in Evolutionary Computation by Peter Korosec Pdf

Evolutionary computation has been widely used in computer science for decades. Even though it started as far back as the 1960s with simulated evolution, the subject is still evolving. During this time, new metaheuristic optimization approaches, like evolutionary algorithms, genetic algorithms, swarm intelligence, etc., were being developed and new fields of usage in artificial intelligence, machine learning, combinatorial and numerical optimization, etc., were being explored. However, even with so much work done, novel research into new techniques and new areas of usage is far from over. This book presents some new theoretical as well as practical aspects of evolutionary computation. This book will be of great value to undergraduates, graduate students, researchers in computer science, and anyone else with an interest in learning about the latest developments in evolutionary computation.

Recent Advances in Evolutionary Multi-objective Optimization

Slim Bechikh,Rituparna Datta,Abhishek Gupta
Recent Advances in Evolutionary Multi objective Optimization Book PDF

Author : Slim Bechikh,Rituparna Datta,Abhishek Gupta
Publisher : Springer
Page : 179 pages
File Size : 44,9 Mb
Release : 2016-08-09
Category : Technology & Engineering
ISBN : 9783319429786

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Recent Advances in Evolutionary Multi-objective Optimization by Slim Bechikh,Rituparna Datta,Abhishek Gupta Pdf

This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-and coming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include: optimization in dynamic environments, multi-objective bilevel programming, handling high dimensionality under many objectives, and evolutionary multitasking. In addition to theory and methodology, this book describes several real-world applications from various domains, which will expose the readers to the versatility of evolutionary multi-objective optimization.

Theory of Evolutionary Computation

Benjamin Doerr,Frank Neumann
Theory of Evolutionary Computation Book PDF

Author : Benjamin Doerr,Frank Neumann
Publisher : Springer Nature
Page : 506 pages
File Size : 46,6 Mb
Release : 2019-11-20
Category : Computers
ISBN : 9783030294144

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Theory of Evolutionary Computation by Benjamin Doerr,Frank Neumann Pdf

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 Computation: Theory and Applications

Xin Yao
Evolutionary Computation Theory and Applications Book PDF

Author : Xin Yao
Publisher : World Scientific
Page : 376 pages
File Size : 55,6 Mb
Release : 1999-11-22
Category : Computers
ISBN : 9789814518161

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Evolutionary Computation: Theory and Applications by Xin Yao Pdf

Evolutionary computation is the study of computational systems which use ideas and get inspiration from natural evolution and adaptation. This book is devoted to the theory and application of evolutionary computation. It is a self-contained volume which covers both introductory material and selected advanced topics. The book can roughly be divided into two major parts: the introductory one and the one on selected advanced topics. Each part consists of several chapters which present an in-depth discussion of selected topics. A strong connection is established between evolutionary algorithms and traditional search algorithms. This connection enables us to incorporate ideas in more established fields into evolutionary algorithms. The book is aimed at a wide range of readers. It does not require previous exposure to the field since introductory material is included. It will be of interest to anyone who is interested in adaptive optimization and learning. People in computer science, artificial intelligence, operations research, and various engineering fields will find it particularly interesting. Contents:Introduction (X Yao)Evolutionary Computation in Behavior Engineering (M Colombetti & M Dorigo)A General Method for Incremental Self-Improvement and Multi-Agent Learning (J Schmidhuber)Teacher: A Genetics-Based System for Learning and for Generalizing Heuristics (B W Wah & A Ieumwananonthachai)Automatic Discovery of Protein Motifs Using Genetic Programming (J R Koza & D Andre)The Role of Self Organization in Evolutionary Computations (A C Tsoi & J Shaw)Virus-Evolutionary Genetic Algorithm and Its Application to Traveling Salesman Problem (T Fukuda et al.)Hybrid Evolutionary Optimization Algorithm for Constrained Problems (J-H Kim & H Myung)CAM-BRAIN — The Evolutionary Engineering of a Billion Neuron Artificial Brain (H de Garis)An Evolutionary Approach to the N-Player Iterated Prisoner's Dilemma Game (X Yao & Darwen) Readership: Graduate students, practitioners and researchers in engineering and electronics and computer science. keywords:Genetic Algorithms;Evolutionary Computation;Evolutionary Algorithms;Genetic Programming;Evolutionary Robotics;Global Optimization;Evolutionary Games;Global Optimization;Machine Learning;Artificial Intelligence

Introduction to Evolutionary Algorithms

Xinjie Yu,Mitsuo Gen
Introduction to Evolutionary Algorithms Book PDF

Author : Xinjie Yu,Mitsuo Gen
Publisher : Springer Science & Business Media
Page : 427 pages
File Size : 45,5 Mb
Release : 2010-06-10
Category : Computers
ISBN : 9781849961295

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Introduction to Evolutionary Algorithms by Xinjie Yu,Mitsuo Gen Pdf

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.

Evolutionary Computation

D. Dumitrescu,Beatrice Lazzerini,Lakhmi C. Jain,A. Dumitrescu
Evolutionary Computation Book PDF

Author : D. Dumitrescu,Beatrice Lazzerini,Lakhmi C. Jain,A. Dumitrescu
Publisher : CRC Press
Page : 424 pages
File Size : 43,8 Mb
Release : 2000-06-22
Category : Computers
ISBN : 0849305888

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Evolutionary Computation by D. Dumitrescu,Beatrice Lazzerini,Lakhmi C. Jain,A. Dumitrescu Pdf

Rapid advances in evolutionary computation have opened up a world of applications-a world rapidly growing and evolving. Decision making, neural networks, pattern recognition, complex optimization/search tasks, scheduling, control, automated programming, and cellular automata applications all rely on evolutionary computation. Evolutionary Computation presents the basic principles of evolutionary computing: genetic algorithms, evolution strategies, evolutionary programming, genetic programming, learning classifier systems, population models, and applications. It includes detailed coverage of binary and real encoding, including selection, crossover, and mutation, and discusses the (m+l) and (m,l) evolution strategy principles. The focus then shifts to applications: decision strategy selection, training and design of neural networks, several approaches to pattern recognition, cellular automata, applications of genetic programming, and more.

Evolutionary Computation for Modeling and Optimization

Daniel Ashlock
Evolutionary Computation for Modeling and Optimization Book PDF

Author : Daniel Ashlock
Publisher : Springer Science & Business Media
Page : 572 pages
File Size : 43,7 Mb
Release : 2006-04-04
Category : Computers
ISBN : 9780387319094

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Evolutionary Computation for Modeling and Optimization by Daniel Ashlock Pdf

Concentrates on developing intuition about evolutionary computation and problem solving skills and tool sets. Lots of applications and test problems, including a biotechnology chapter.

Evolutionary Learning: Advances in Theories and Algorithms

Zhi-Hua Zhou,Yang Yu,Chao Qian
Evolutionary Learning Advances in Theories and Algorithms Book PDF

Author : Zhi-Hua Zhou,Yang Yu,Chao Qian
Publisher : Springer
Page : 361 pages
File Size : 46,5 Mb
Release : 2019-05-22
Category : Computers
ISBN : 9789811359569

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Evolutionary Learning: Advances in Theories and Algorithms by Zhi-Hua Zhou,Yang Yu,Chao Qian Pdf

Many machine learning tasks involve solving complex optimization problems, such as working on non-differentiable, non-continuous, and non-unique objective functions; in some cases it can prove difficult to even define an explicit objective function. Evolutionary learning applies evolutionary algorithms to address optimization problems in machine learning, and has yielded encouraging outcomes in many applications. However, due to the heuristic nature of evolutionary optimization, most outcomes to date have been empirical and lack theoretical support. This shortcoming has kept evolutionary learning from being well received in the machine learning community, which favors solid theoretical approaches. Recently there have been considerable efforts to address this issue. This book presents a range of those efforts, divided into four parts. Part I briefly introduces readers to evolutionary learning and provides some preliminaries, while Part II presents general theoretical tools for the analysis of running time and approximation performance in evolutionary algorithms. Based on these general tools, Part III presents a number of theoretical findings on major factors in evolutionary optimization, such as recombination, representation, inaccurate fitness evaluation, and population. In closing, Part IV addresses the development of evolutionary learning algorithms with provable theoretical guarantees for several representative tasks, in which evolutionary learning offers excellent performance.

Parameter Setting in Evolutionary Algorithms

F.J. Lobo,Cláudio F. Lima,Zbigniew Michalewicz
Parameter Setting in Evolutionary Algorithms Book PDF

Author : F.J. Lobo,Cláudio F. Lima,Zbigniew Michalewicz
Publisher : Springer
Page : 318 pages
File Size : 50,7 Mb
Release : 2007-04-03
Category : Technology & Engineering
ISBN : 9783540694328

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Parameter Setting in Evolutionary Algorithms by F.J. Lobo,Cláudio F. Lima,Zbigniew Michalewicz Pdf

One of the main difficulties of applying an evolutionary algorithm (or, as a matter of fact, any heuristic method) to a given problem is to decide on an appropriate set of parameter values. Typically these are specified before the algorithm is run and include population size, selection rate, operator probabilities, not to mention the representation and the operators themselves. This book gives the reader a solid perspective on the different approaches that have been proposed to automate control of these parameters as well as understanding their interactions. The book covers a broad area of evolutionary computation, including genetic algorithms, evolution strategies, genetic programming, estimation of distribution algorithms, and also discusses the issues of specific parameters used in parallel implementations, multi-objective evolutionary algorithms, and practical consideration for real-world applications. It is a recommended read for researchers and practitioners of evolutionary computation and heuristic methods.