Advances in Evolutionary Computing

Advances in Evolutionary Computing
Author: Ashish Ghosh,Shigeyoshi Tsutsui
Publsiher: Springer Science & Business Media
Total Pages: 1006
Release: 2012-12-06
Genre: Computers
ISBN: 9783642189654

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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.

Evolutionary Computation

Evolutionary Computation
Author: D. Dumitrescu,Beatrice Lazzerini,Lakhmi C. Jain,A. Dumitrescu
Publsiher: CRC Press
Total Pages: 424
Release: 2000-06-22
Genre: Computers
ISBN: 0849305888

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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.

New Achievements in Evolutionary Computation

New Achievements in Evolutionary Computation
Author: Peter Korosec
Publsiher: BoD – Books on Demand
Total Pages: 328
Release: 2010-02-01
Genre: Computers
ISBN: 9789533070537

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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.

Introduction to Evolutionary Computing

Introduction to Evolutionary Computing
Author: Agoston E. Eiben,J.E. Smith
Publsiher: Springer Science & Business Media
Total Pages: 300
Release: 2013-03-14
Genre: Computers
ISBN: 9783662050941

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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.

Towards a New Evolutionary Computation

Towards a New Evolutionary Computation
Author: Jose A. Lozano,Pedro Larrañaga,Iñaki Inza,Endika Bengoetxea
Publsiher: Springer
Total Pages: 294
Release: 2006-01-21
Genre: Technology & Engineering
ISBN: 9783540324942

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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.

Recent Advances in Evolutionary Multi objective Optimization

Recent Advances in Evolutionary Multi objective Optimization
Author: Slim Bechikh,Rituparna Datta,Abhishek Gupta
Publsiher: Springer
Total Pages: 179
Release: 2016-08-09
Genre: Technology & Engineering
ISBN: 9783319429786

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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.

Recent Advances in Swarm Intelligence and Evolutionary Computation

Recent Advances in Swarm Intelligence and Evolutionary Computation
Author: Xin-She Yang
Publsiher: Springer
Total Pages: 300
Release: 2014-12-27
Genre: Technology & Engineering
ISBN: 9783319138268

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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.

Evolutionary Computation

Evolutionary Computation
Author: Xin Yao
Publsiher: World Scientific
Total Pages: 360
Release: 1999
Genre: Science
ISBN: 9810223064

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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.

Advances in Evolutionary Computing for System Design

Advances in Evolutionary Computing for System Design
Author: Vasile Palade,Dipti Srinivasan
Publsiher: Springer
Total Pages: 326
Release: 2007-07-07
Genre: Computers
ISBN: 9783540723776

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Evolutionary computing paradigms offer robust and powerful adaptive search mechanisms for system design. This book’s thirteen chapters cover a wide area of topics in evolutionary computing and applications, including an introduction to evolutionary computing in system design; evolutionary neuro-fuzzy systems; and evolution of fuzzy controllers. The book will be useful to researchers in intelligent systems with interest in evolutionary computing, as well as application engineers and system designers.

Recent Advances in Evolutionary Computation for Combinatorial Optimization

Recent Advances in Evolutionary Computation for Combinatorial Optimization
Author: Carlos Cotta
Publsiher: Springer Science & Business Media
Total Pages: 336
Release: 2008-08-26
Genre: Business & Economics
ISBN: 9783540708063

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This cutting-edge volume presents recent advances in the area of metaheuristic combinatorial optimisation, with a special focus on evolutionary computation methods. Moreover, it addresses local search methods and hybrid approaches.

Evolutionary Multiobjective Optimization

Evolutionary Multiobjective Optimization
Author: Ajith Abraham,Robert Goldberg
Publsiher: Springer Science & Business Media
Total Pages: 302
Release: 2006-03-30
Genre: Computers
ISBN: 9781846281372

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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.

Estimation of Distribution Algorithms

Estimation of Distribution Algorithms
Author: Pedro Larrañaga,José A. Lozano
Publsiher: Springer Science & Business Media
Total Pages: 382
Release: 2012-12-06
Genre: Computers
ISBN: 9781461515395

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Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is devoted to a new paradigm for evolutionary computation, named estimation of distribution algorithms (EDAs). This new class of algorithms generalizes genetic algorithms by replacing the crossover and mutation operators with learning and sampling from the probability distribution of the best individuals of the population at each iteration of the algorithm. Working in such a way, the relationships between the variables involved in the problem domain are explicitly and effectively captured and exploited. This text constitutes the first compilation and review of the techniques and applications of this new tool for performing evolutionary computation. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is clearly divided into three parts. Part I is dedicated to the foundations of EDAs. In this part, after introducing some probabilistic graphical models - Bayesian and Gaussian networks - a review of existing EDA approaches is presented, as well as some new methods based on more flexible probabilistic graphical models. A mathematical modeling of discrete EDAs is also presented. Part II covers several applications of EDAs in some classical optimization problems: the travelling salesman problem, the job scheduling problem, and the knapsack problem. EDAs are also applied to the optimization of some well-known combinatorial and continuous functions. Part III presents the application of EDAs to solve some problems that arise in the machine learning field: feature subset selection, feature weighting in K-NN classifiers, rule induction, partial abductive inference in Bayesian networks, partitional clustering, and the search for optimal weights in artificial neural networks. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is a useful and interesting tool for researchers working in the field of evolutionary computation and for engineers who face real-world optimization problems. This book may also be used by graduate students and researchers in computer science. `... I urge those who are interested in EDAs to study this well-crafted book today.' David E. Goldberg, University of Illinois Champaign-Urbana.

Parameter Setting in Evolutionary Algorithms

Parameter Setting in Evolutionary Algorithms
Author: F.J. Lobo,Cláudio F. Lima,Zbigniew Michalewicz
Publsiher: Springer Science & Business Media
Total Pages: 318
Release: 2007-03-16
Genre: Mathematics
ISBN: 9783540694311

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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.

Advances of Evolutionary Computation Methods and Operators

Advances of Evolutionary Computation  Methods and Operators
Author: Erik Cuevas,Margarita Arimatea Díaz Cortés,Diego Alberto Oliva Navarro
Publsiher: Springer
Total Pages: 202
Release: 2016-01-20
Genre: Technology & Engineering
ISBN: 9783319285030

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The goal of this book is to present advances that discuss alternative Evolutionary Computation (EC) developments and non-conventional operators which have proved to be effective in the solution of several complex problems. The book has been structured so that each chapter can be read independently from the others. The book contains nine chapters with the following themes: 1) Introduction, 2) the Social Spider Optimization (SSO), 3) the States of Matter Search (SMS), 4) the collective animal behavior (CAB) algorithm, 5) the Allostatic Optimization (AO) method, 6) the Locust Search (LS) algorithm, 7) the Adaptive Population with Reduced Evaluations (APRE) method, 8) the multimodal CAB, 9) the constrained SSO method.

Advances in Evolutionary Algorithms

Advances in Evolutionary Algorithms
Author: Chang Wook Ahn
Publsiher: Springer
Total Pages: 172
Release: 2007-05-22
Genre: Technology & Engineering
ISBN: 9783540317593

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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.