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Introduction to Evolutionary Algorithms

Introduction to Evolutionary Algorithms Author Xinjie Yu
ISBN-10 1849961298
Release 2010-06-10
Pages 422
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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.

Introduction to Evolutionary Computing

Introduction to Evolutionary Computing Author A.E. Eiben
ISBN-10 9783662448748
Release 2015-07-01
Pages 287
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The overall structure of this new edition is three-tier: Part I presents the basics, Part II is concerned with methodological issues, and Part III discusses advanced topics. In the second edition the authors have reorganized the material to focus on problems, how to represent them, and then how to choose and design algorithms for different representations. They also added a chapter on problems, reflecting the overall book focus on problem-solvers, a chapter on parameter tuning, which they combined with the parameter control and "how-to" chapters into a methodological part, and finally a chapter on evolutionary robotics with an outlook on possible exciting developments in this field. The book is suitable for undergraduate and graduate courses in artificial intelligence and computational intelligence, and for self-study by practitioners and researchers engaged with all aspects of bioinspired design and optimization.

Evolutionary Computation for Modeling and Optimization

Evolutionary Computation for Modeling and Optimization Author Daniel Ashlock
ISBN-10 0387319093
Release 2006-04-04
Pages 572
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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 Intelligence

Evolutionary Intelligence Author S. Sumathi
ISBN-10 9783540753827
Release 2008
Pages 584
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Evolutionary Intelligence has been writing in one form or another for most of life. You can find so many inspiration from Evolutionary Intelligence also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Evolutionary Intelligence book for free.

Evolutionary Algorithms for Mobile Ad Hoc Networks

Evolutionary Algorithms for Mobile Ad Hoc Networks Author Bernabé Dorronsoro
ISBN-10 9781118832028
Release 2014-04-08
Pages 240
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Describes how evolutionary algorithms (EAs) can be used to identify, model, and minimize day-to-day problems that arise for researchers in optimization and mobile networking Mobile ad hoc networks (MANETs), vehicular networks (VANETs), sensor networks (SNs), and hybrid networks—each of these require a designer’s keen sense and knowledge of evolutionary algorithms in order to help with the common issues that plague professionals involved in optimization and mobile networking. This book introduces readers to both mobile ad hoc networks and evolutionary algorithms, presenting basic concepts as well as detailed descriptions of each. It demonstrates how metaheuristics and evolutionary algorithms (EAs) can be used to help provide low-cost operations in the optimization process—allowing designers to put some “intelligence” or sophistication into the design. It also offers efficient and accurate information on dissemination algorithms, topology management, and mobility models to address challenges in the field. Evolutionary Algorithms for Mobile Ad Hoc Networks: Instructs on how to identify, model, and optimize solutions to problems that arise in daily research Presents complete and up-to-date surveys on topics like network and mobility simulators Provides sample problems along with solutions/descriptions used to solve each, with performance comparisons Covers current, relevant issues in mobile networks, like energy use, broadcasting performance, device mobility, and more Evolutionary Algorithms for Mobile Ad Hoc Networks is an ideal book for researchers and students involved in mobile networks, optimization, advanced search techniques, and multi-objective optimization.

Evolutionary Computation in Bioinformatics

Evolutionary Computation in Bioinformatics Author Gary Fogel
ISBN-10 1558607978
Release 2003
Pages 393
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This book offers a definitive resource that bridges biology and evolutionary computation. The authors have written an introduction to biology and bioinformatics for computer scientists, plus an introduction to evolutionary computation for biologists and for computer scientists unfamiliar with these techniques.

Applications of Evolutionary Computation in Chemistry

Applications of Evolutionary Computation in Chemistry Author Roy L. Johnston
ISBN-10 3540402586
Release 2004-03-12
Pages 184
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H. M. Cartwright: An Introduction to Evolutionary Computation andEvolutionary Algorithms; B. Hartke: Application of Evolutionary Algorithms to Global Cluster Geometry Optimization; K.D.M. Harris, R.L. Johnston, S. Habershon: Application of Evolutionary Computation in Structure Solution from Diffraction Data; S. M.

Illustrating Evolutionary Computation with Mathematica

Illustrating Evolutionary Computation with Mathematica Author Christian Jacob
ISBN-10 9781558606371
Release 2001
Pages 578
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An essential capacity of intelligence is the ability to learn. An artificially intelligent system that could learn would not have to be programmed for every eventuality; it could adapt to its changing environment and conditions just as biological systems do. Illustrating Evolutionary Computation with Mathematica introduces evolutionary computation to the technically savvy reader who wishes to explore this fascinating and increasingly important field. Unique among books on evolutionary computation, the book also explores the application of evolution to developmental processes in nature, such as the growth processes in cells and plants. If you are a newcomer to the evolutionary computation field, an engineer, a programmer, or even a biologist wanting to learn how to model the evolution and coevolution of plants, this book will provide you with a visually rich and engaging account of this complex subject. * Introduces the major mechanisms of biological evolution. * Demonstrates many fascinating aspects of evolution in nature with simple, yet illustrative examples. * Explains each of the major branches of evolutionary computation: genetic algorithms, genetic programming, evolutionary programming, and evolution strategies. * Demonstrates the programming of computers by evolutionary principles using Evolvica, a genetic programming system designed by the author. * Shows in detail how to evolve developmental programs modeled by cellular automata and Lindenmayer systems. * Provides Mathematica notebooks on the Web that include all the programs in the book and supporting animations, movies, and graphics.

An Introduction to Genetic Algorithms

An Introduction to Genetic Algorithms Author Melanie Mitchell
ISBN-10 0262631857
Release 1998
Pages 209
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Genetic algorithms are used in science and engineering for problem solving and as computational models. This brief introduction enables readers to implement and experiment with genetic algorithms on their own. The descriptions of applications and modeling projects stretch beyond the boundaries of computer science to include systems theory, game theory, biology, ecology, and population genetics. 20 illustrations.

Evolutionary Computation

Evolutionary Computation Author Kenneth A. De Jong
ISBN-10 9780262041942
Release 2006-02-03
Pages 256
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This text is an introduction to the field of evolutionary computation. It approaches evolution strategies and genetic programming, as instances of a more general class of evolutionary algorithms.

Pattern Mining with Evolutionary Algorithms

Pattern Mining with Evolutionary Algorithms Author Sebastián Ventura
ISBN-10 9783319338583
Release 2016-06-13
Pages 190
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This book provides a comprehensive overview of the field of pattern mining with evolutionary algorithms. To do so, it covers formal definitions about patterns, patterns mining, type of patterns and the usefulness of patterns in the knowledge discovery process. As it is described within the book, the discovery process suffers from both high runtime and memory requirements, especially when high dimensional datasets are analyzed. To solve this issue, many pruning strategies have been developed. Nevertheless, with the growing interest in the storage of information, more and more datasets comprise such a dimensionality that the discovery of interesting patterns becomes a challenging process. In this regard, the use of evolutionary algorithms for mining pattern enables the computation capacity to be reduced, providing sufficiently good solutions. This book offers a survey on evolutionary computation with particular emphasis on genetic algorithms and genetic programming. Also included is an analysis of the set of quality measures most widely used in the field of pattern mining with evolutionary algorithms. This book serves as a review of the most important evolutionary algorithms for pattern mining. It considers the analysis of different algorithms for mining different type of patterns and relationships between patterns, such as frequent patterns, infrequent patterns, patterns defined in a continuous domain, or even positive and negative patterns. A completely new problem in the pattern mining field, mining of exceptional relationships between patterns, is discussed. In this problem the goal is to identify patterns which distribution is exceptionally different from the distribution in the complete set of data records. Finally, the book deals with the subgroup discovery task, a method to identify a subgroup of interesting patterns that is related to a dependent variable or target attribute. This subgroup of patterns satisfies two essential conditions: interpretability and interestingness.

Meta heuristic and Evolutionary Algorithms for Engineering Optimization

Meta heuristic and Evolutionary Algorithms for Engineering Optimization Author Omid Bozorg-Haddad
ISBN-10 9781119386995
Release 2017-10-09
Pages 304
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Overview of optimization -- Introduction to meta-heuristic and evolutionary algorithms -- Pattern search (PS) -- Genetic algorithm (GA) -- Simulated annealing (SA) -- Tabu search (TS) -- Ant colony optimization (ACO) -- Particle swarm optimization (PSO) -- Differential evolution (DE) -- Harmony search (HS) -- Shuffled frog-leaping algorithm (SFLA) -- Honey-bee mating optimization (HBMO) -- Invasive weed optimization (IWO) -- Central force optimization (CFO) -- Biogeography-based optimization (BBO) -- Firefly algorithm (FA) -- Gravity search algorithm (GSA) -- Bat algorithm (BA) -- Plant propagation algorithm (PPA) -- Water cycle algorithm (WCA) -- Symbiotic organisms search (SOS) -- Comprehensive evolutionary algorithm (CEA)

Evolutionary Statistical Procedures

Evolutionary Statistical Procedures Author Roberto Baragona
ISBN-10 3642162185
Release 2011-01-03
Pages 276
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This proposed text appears to be a good introduction to evolutionary computation for use in applied statistics research. The authors draw from a vast base of knowledge about the current literature in both the design of evolutionary algorithms and statistical techniques. Modern statistical research is on the threshold of solving increasingly complex problems in high dimensions, and the generalization of its methodology to parameters whose estimators do not follow mathematically simple distributions is underway. Many of these challenges involve optimizing functions for which analytic solutions are infeasible. Evolutionary algorithms represent a powerful and easily understood means of approximating the optimum value in a variety of settings. The proposed text seeks to guide readers through the crucial issues of optimization problems in statistical settings and the implementation of tailored methods (including both stand-alone evolutionary algorithms and hybrid crosses of these procedures with standard statistical algorithms like Metropolis-Hastings) in a variety of applications. This book would serve as an excellent reference work for statistical researchers at an advanced graduate level or beyond, particularly those with a strong background in computer science.

Applied Evolutionary Algorithms in Java

Applied Evolutionary Algorithms in Java Author Robert Ghanea-Hercock
ISBN-10 9780387216157
Release 2013-03-20
Pages 219
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This book is intended for students, researchers, and professionals interested in evolutionary algorithms at graduate and postgraduate level. No mathematics beyond basic algebra and Cartesian graphs methods is required, as the aim is to encourage applying the JAVA toolkit to develop an appreciation of the power of these techniques.

Evolutionary Algorithms in Management Applications

Evolutionary Algorithms in Management Applications Author Jörg Biethahn
ISBN-10 9783642612176
Release 2012-12-06
Pages 379
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Evolutionary Algorithms (EA) are powerful search and optimisation techniques inspired by the mechanisms of natural evolution. They imitate, on an abstract level, biological principles such as a population based approach, the inheritance of information, the variation of information via crossover/mutation, and the selection of individuals based on fitness. The most well-known class of EA are Genetic Algorithms (GA), which have received much attention not only in the scientific community lately. Other variants of EA, in particular Genetic Programming, Evolution Strategies, and Evolutionary Programming are less popular, though very powerful too. Traditionally, most practical applications of EA have appeared in the technical sector. Management problems, for a long time, have been a rather neglected field of EA-research. This is surprising, since the great potential of evolutionary approaches for the business and economics domain was recognised in pioneering publications quite a while ago. John Holland, for instance, in his seminal book Adaptation in Natural and Artificial Systems (The University of Michigan Press, 1975) identified economics as one of the prime targets for a theory of adaptation, as formalised in his reproductive plans (later called Genetic Algorithms).

Evolutionary Computation 1

Evolutionary Computation 1 Author Thomas Baeck
ISBN-10 0750306645
Release 2000-01-01
Pages 378
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The field of evolutionary computation is expanding dramatically, fueled by the vast investment that reflects the value of applying its techniques. Culling material from the Handbook of Evolutionary Computation, Evolutionary Computation 1: Basic Algorithms and Operators contains up-to-date information on algorithms and operators used in evolutionary computing. This volume discusses the basic ideas that underlie the main paradigms of evolutionary algorithms, evolution strategies, evolutionary programming, and genetic programming. It is intended to be used by individual researchers, teachers, and students working and studying in this expanding field.

Evolutionary Algorithms in Theory and Practice

Evolutionary Algorithms in Theory and Practice Author Thomas Bäck
ISBN-10 9780195099713
Release 1996
Pages 314
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A comparison of evolutionary algorithms. Organic evolution and problem solving. Biological background. Evolutionary algorithms and artificial intelligence. Evolutionary algorithms and global optimization. Early approaches. Specific evolutionary algorithms. Evolution strategies. Evolutionary programming. Genetic algorithms. Artificial landscapes. An empirical comparison. Extending genetic algorithms. Selection. Selection mechanisms. Experimental investigation of selection. Mutation. Simplified genetic algorithms. An experiment in meta-evolution. Summary and outlook. Data for the fletcher-powell function. Data from selection experiments. Software. The multiprocessor environment; mathematical symbols.