Evolutionary Algorithms And Chaotic Systems PDF Download
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| Author | : Ivan Zelinka |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 533 |
| Release | : 2010-02-23 |
| Genre | : Computers |
| ISBN | : 3642107060 |
Download Evolutionary Algorithms and Chaotic Systems Book in PDF, ePub and Kindle
This book discusses the mutual intersection of two fields of research: evolutionary computation, which can handle tasks such as control of various chaotic systems, and deterministic chaos, which is investigated as a behavioral part of evolutionary algorithms.
| Author | : Ivan Zelinka |
| Publisher | : Springer |
| Total Pages | : 322 |
| Release | : 2017-11-25 |
| Genre | : Technology & Engineering |
| ISBN | : 3662556634 |
Download Evolutionary Algorithms, Swarm Dynamics and Complex Networks Book in PDF, ePub and Kindle
Evolutionary algorithms constitute a class of well-known algorithms, which are designed based on the Darwinian theory of evolution and Mendelian theory of heritage. They are partly based on random and partly based on deterministic principles. Due to this nature, it is challenging to predict and control its performance in solving complex nonlinear problems. Recently, the study of evolutionary dynamics is focused not only on the traditional investigations but also on the understanding and analyzing new principles, with the intention of controlling and utilizing their properties and performances toward more effective real-world applications. In this book, based on many years of intensive research of the authors, is proposing novel ideas about advancing evolutionary dynamics towards new phenomena including many new topics, even the dynamics of equivalent social networks. In fact, it includes more advanced complex networks and incorporates them with the CMLs (coupled map lattices), which are usually used for spatiotemporal complex systems simulation and analysis, based on the observation that chaos in CML can be controlled, so does evolution dynamics. All the chapter authors are, to the best of our knowledge, originators of the ideas mentioned above and researchers on evolutionary algorithms and chaotic dynamics as well as complex networks, who will provide benefits to the readers regarding modern scientific research on related subjects.
| Author | : Xinjie Yu |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 427 |
| Release | : 2010-06-10 |
| Genre | : Computers |
| ISBN | : 1849961298 |
Download Introduction to Evolutionary Algorithms Book in PDF, ePub and Kindle
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.
| Author | : Alfredo Milani |
| Publisher | : MDPI |
| Total Pages | : 144 |
| Release | : 2020-12-07 |
| Genre | : Technology & Engineering |
| ISBN | : 3039436112 |
Download Evolutionary Algorithms in Intelligent Systems Book in PDF, ePub and Kindle
Evolutionary algorithms and metaheuristics are widely used to provide efficient and effective approximate solutions to computationally hard optimization problems. With the widespread use of intelligent systems in recent years, evolutionary algorithms have been applied, beyond classical optimization problems, to AI system parameter optimization and the design of artificial neural networks and feature selection in machine learning systems. This volume will present recent results of applications of the most successful metaheuristics, from differential evolution and particle swarm optimization to artificial neural networks, loT allocation, and multi-objective optimization problems. It will also provide a broad view of the role and the potential of evolutionary algorithms as service components in Al systems.
| Author | : Carlos Coello Coello |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 600 |
| Release | : 2013-03-09 |
| Genre | : Computers |
| ISBN | : 1475751842 |
Download Evolutionary Algorithms for Solving Multi-Objective Problems Book in PDF, ePub and Kindle
Researchers and practitioners alike are increasingly turning to search, op timization, and machine-learning procedures based on natural selection and natural genetics to solve problems across the spectrum of human endeavor. These genetic algorithms and techniques of evolutionary computation are solv ing problems and inventing new hardware and software that rival human designs. The Kluwer Series on Genetic Algorithms and Evolutionary Computation pub lishes research monographs, edited collections, and graduate-level texts in this rapidly growing field. Primary areas of coverage include the theory, implemen tation, and application of genetic algorithms (GAs), evolution strategies (ESs), evolutionary programming (EP), learning classifier systems (LCSs) and other variants of genetic and evolutionary computation (GEC). The series also pub lishes texts in related fields such as artificial life, adaptive behavior, artificial immune systems, agent-based systems, neural computing, fuzzy systems, and quantum computing as long as GEC techniques are part of or inspiration for the system being described. This encyclopedic volume on the use of the algorithms of genetic and evolu tionary computation for the solution of multi-objective problems is a landmark addition to the literature that comes just in the nick of time. Multi-objective evolutionary algorithms (MOEAs) are receiving increasing and unprecedented attention. Researchers and practitioners are finding an irresistible match be tween the popUlation available in most genetic and evolutionary algorithms and the need in multi-objective problems to approximate the Pareto trade-off curve or surface.
| Author | : Tobias Blickle |
| Publisher | : vdf Hochschulverlag AG |
| Total Pages | : 278 |
| Release | : 1997 |
| Genre | : Computers |
| ISBN | : 9783728124333 |
Download Theory of Evolutionary Algorithms and Application to System Synthesis Book in PDF, ePub and Kindle
| Author | : Roman Senkerik |
| Publisher | : LAP Lambert Academic Publishing |
| Total Pages | : 264 |
| Release | : 2009-09 |
| Genre | : |
| ISBN | : 9783838313658 |
Download Optimal Control of Discrete Chaotic Systems Book in PDF, ePub and Kindle
The problem of control of chaos has attracted the attention of researchers and engineers, and many methods have been developed since the early 1990 s. The main aim of this book is to show that evolutionary algorithms (EA) which is a powerful tool for almost any difficult and complex optimization problem can be in reality be used for the optimization of deterministic chaos control. This book aims to show how to use EA and how to properly define the cost function. It is also focused on the selection of control methods and the explanation of all possible problems which arises in such a difficult task of chaos control optimization. This book contains examples of EA implementation to methods for chaos control for the purpose of obtaining better results. This implies faster reaching of desired state and superior stabilization, which could be robust and effective means to optimize difficult practical problems. This book introduces a different approach to the challenging task of chaos control, and should assist students, academic researchers and engineers working with either nonlinear and chaotic systems, or evolutionary computation.
| Author | : David G. Mayer |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 110 |
| Release | : 2012-12-06 |
| Genre | : Computers |
| ISBN | : 1461517176 |
Download Evolutionary Algorithms and Agricultural Systems Book in PDF, ePub and Kindle
Evolutionary Algorithms and Agricultural Systems deals with the practical application of evolutionary algorithms to the study and management of agricultural systems. The rationale of systems research methodology is introduced, and examples listed of real-world applications. It is the integration of these agricultural systems models with optimization techniques, primarily genetic algorithms, which forms the focus of this book. The advantages are outlined, with examples of agricultural models ranging from national and industry-wide studies down to the within-farm scale. The potential problems of this approach are also discussed, along with practical methods of resolving these problems. Agricultural applications using alternate optimization techniques (gradient and direct-search methods, simulated annealing and quenching, and the tabu search strategy) are also listed and discussed. The particular problems and methodologies of these algorithms, including advantageous features that may benefit a hybrid approach or be usefully incorporated into evolutionary algorithms, are outlined. From consideration of this and the published examples, it is concluded that evolutionary algorithms are the superior method for the practical optimization of models of agricultural and natural systems. General recommendations on robust options and parameter settings for evolutionary algorithms are given for use in future studies. Evolutionary Algorithms and Agricultural Systems will prove useful to practitioners and researchers applying these methods to the optimization of agricultural or natural systems, and would also be suited as a text for systems management, applied modeling, or operations research.
| Author | : Peter Bentley |
| Publisher | : Morgan Kaufmann |
| Total Pages | : 618 |
| Release | : 2002 |
| Genre | : Computers |
| ISBN | : 1558606734 |
Download Creative Evolutionary Systems Book in PDF, ePub and Kindle
Written for computer scientists and students, and computer literate artists, designers and specialists in evolutionary computation, this text brings together the most advanced work in the use of evolutionary computation for creative results.
| Author | : Melanie Mitchell |
| Publisher | : MIT Press |
| Total Pages | : 226 |
| Release | : 1998-03-02 |
| Genre | : Computers |
| ISBN | : 9780262631853 |
Download An Introduction to Genetic Algorithms Book in PDF, ePub and Kindle
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics—particularly in machine learning, scientific modeling, and artificial life—and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.
