Book Description
Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run.
· Comprehensive coverage of this growing area of research
· Carefully introduces each algorithm with examples and in-depth discussion
· Includes many applications to real-world problems, including engineering design and scheduling
· Includes discussion of advanced topics and future research
· Can be used as a course text or for self-study
· Accessible to those with limited knowledge of classical multi-objective optimization and evolutionary algorithms
The integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
Customer Reviews:
Great Book.......2007-02-26
I highly recommend this book, it covers all the important subjects. A great acquisition!
Great book; a must for engineers and scientists alike.......2001-09-28
Kalyanmoy Deb has put together a great summary of the state of affairs in multiobjective genetic algorithms. Should you be an engineer or a scientist involved in the optimization of any design of sizeable complexity, you should read this book and become familiar with the techniques that have evolved over the last decade into powerful methods of optimization. This book is in many many ways bridging the gap from Michalewicz's and Fogel's book ("How to solve it") to the more modern era of this field (eg late nineties up to now...). So whereas those two authors never really considered multiobjective genetic algorithms, Deb plows through with the great expertize of a (perhaps even "the") leading researcher in that domain. This is a great book of _receipes_ with the level of details necessary to make use of them. It's a "how to" book; this is the one you have cracked open on your desk while you're hard coding it all up. However, it's not very well written with the prose being very terse and basically quite unengaging. But so what! In some sense yes perhaps, but Michalewicz and Fogel made a point that one can write technical litterature that one can also read. Perhaps they went overboard... in any case, Deb's book is about algorithms so who cares about whether the book puts you to sleep and it can do that, unfortunately. Apart from the unengaging style and the paucity of depth in the examples scope, the real problem with the book is not with the book itself, it's with the field of multiobjective optimization based on evolutionary methods. It's fairly evident that there is not much of any sort of fundamental understanding available at this time in support of why evolutionary techniques do work well, and they do, sometimes... If this understanding is available, you won't find it in Deb's book. If you are like me though, you won't care all that much really so long as the techniques are efficient and presented in a way that make them useable, and that's done right... But on the whole, it's a little unsatisfying because one's left with a panoply of various techniques and ways to define operators and representations but there is no insight given on which one might be best or how to craft them to particular situations. There is a lot of so-'n-so in reference this and that did it like this and it seems to work well there, however... The reason for this state of affairs is, of course, that nobody has a real clue, yet... But that is _not_ Deb's fault and this is not why, as a user, I'm not rating his book a full 5 stars. In some sense it could be rated as high as that but I thought the presentation was rather unengaging and not with all the breath and depth it could have had. So it's a 4.5 stars perhaps... let's say... but Amazon does not let me select 4.5 stars so it's 4, this edition at least...
The Reference in Evolutionary Multiobjective Optimization.......2001-07-23
This is the first complete and updated text on Multi-objective Evolutionary Algorithms (MOEAs), covering all major areas clearly, thoughtfully and thoroughly. Thanks to the development of evolutionary computation MOEAs are now a well established technique for multi-objective optimization that finds multiple effective solutions in a single run. The widely interdisciplinary interest of engineers, scientists and mathematicians towards MOEAs has been evident during the first international conference on this topic (EMO2001,Zurich). The book is extremely useful for researchers working on multi-objective optimization in all branches of engineering and sciences, that will find a complete description of all available methodologies, starting from a detailed description and criticism of classical methods, towards a deep treating of the most advanced evolutionary techniques. Moreover several analytical test cases are given, covering all difficulties a MOEA encounters when converging towards the Pareto Optimal front. This set of test problems, together with several performance measurement parameters are essential when testing a new strategy before its application to a real-world problem. Despite the detail in advanced topics, Deb's book may be also used as a reference-book for a post-graduate course thanks to the scholarly coverage of basic arguments. As a final remark I strongly suggest everyone working on evolutionary computation and optimization to keep this book on the desk.
Average customer rating:
- Good introduction book for DE
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Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Kenneth V. Price ,
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Multi-Objective Optimization Using Evolutionary Algorithms
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Introduction to Evolutionary Computing (Natural Computing Series)
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Foundations of Genetic Programming
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Ant Colony Optimization (Bradford Books)
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Genetic Algorithms in Search, Optimization, and Machine Learning
Accessories:
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DNA Computing: 12th International Meeting on DNA Computing, DNA12, Seoul, Korea, June 5-9, 2006, Revised Selected Papers (Lecture Notes in Computer Science)
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STACS 2007: 24th Annual Symposium on Theoretical Aspects of Computer Science, Aachen, Germany, February 22-24, 2007, Proceedings (Lecture Notes in Computer Science)
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Membrane Computing: 7th International Workshop, WMC 2006, Leiden, Netherlands, July 17-21, 2006, Revised, Selected, and Invited Papers (Lecture Notes in Computer Science)
ASIN: 3540209506 |
Book Description
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. A companion CD includes DE-based optimization software in several programming languages.
Customer Reviews:
Good introduction book for DE.......2006-03-01
Some one who wants to beagin with DE. This the good starting point. Book started with good conceptual backgroud and carried away with codeing details of DE. Kenneth puts enough efforts to clear concept behind DE. Only thing missing is that book demands little background with GAs, EAs and optimization theory.Other wise nice book for those who are familiarized with concept of evolutionary techniques.
Book Description
Linear Genetic Programming presents a variant of genetic programming (GP) that evolves imperative computer programs as linear sequences of instructions, in contrast to the more traditional functional expressions or syntax trees. Primary characteristics of linear program structure are exploited to achieve acceleration of both execution time and evolutionary progress. Online analysis and optimization of program code lead to more efficient techniques and contribute to a better understanding of the method and its parameters. In particular, the reduction of structural variation step size and non-effective variations play a key role in finding higher quality and less complex solutions. Typical GP phenomena, such as non-effective code, neutral variations, and code growth are investigated from the perspective of linear GP.
This book serves as a reference for researchers; it also contains sufficient introductory material for students and those who are new to the field.
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Applications Of Multi-Objective Evolutionary Algorithms (Advances in Natural Computation)
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ASIN: 9812561064 |
Book Description
This book presents an extensive variety of multi-objective problems across diverse disciplines, along with statistical solutions using multi-objective evolutionary algorithms (MOEAs). The topics discussed serve to promote a wider understanding as well as the use of MOEAs, the aim being to find good solutions for high-dimensional real-world design applications. The book contains a large collection of MOEA applications from many researchers, and thus provides the practitioner with detailed algorithmic direction to achieve good results in their selected problem domain.
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Evolutionary Optimization (International Series in Operations Research & Management Science)
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ASIN: 0792376544 |
Book Description
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.
Customer Reviews:
EAs for Optimization.......2003-05-14
Its a great book which covers cross disciplinary research outcomes. The book is designed like a text book although the chapters were written by different leading researchers in the world. It covers from basic optimizations concepts to complex applications of EAs to theoretical and practical optimization problems. The book is suitable for new researcher or post-grade students in Operations Research / Management Science, Optimization, Industrial Engineering and Computer Science.
Amazon.com
Zbigniew Michalewicz's Genetic Algorithms + Data Structures = Evolution Programs has three sections. The first section is a straightforward introduction to genetic algorithms. In the second section, Michalewicz describes how to apply genetic algorithms to numerical optimization. Michalewicz, who is a pioneer in this field, discusses the rationale for using genetic algorithms for numerical optimization and describes several experiments that show how this new type of genetic algorithm performs. The author devotes the third section of the book to evolution programs.
Book Description
Genetic algorithms are founded upon the principle of evolution, i.e., survival of the fittest. Hence evolution programming techniques, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the traveling salesman problem, and problems of scheduling, partitioning, and control. The importance of these techniques is still growing, since evolution programs are parallel in nature, and parallelism is one of the most promising directions in computer science.
The book is self-contained and the only prerequisite is basic undergraduate mathematics. This third edition has been substantially revised and extended by three new chapters and by additional appendices containing working material to cover recent developments and a change in the perception of evolutionary computation.
Customer Reviews:
Lot of ideas for Genetic and Evolutionary Algorithm.......2007-01-05
This book is not written to be the primary text book for a Genetic Algotithm, Data Structure or a Neural Algorithm course. However this book gives an excellent introduction to modern approaches to Evolutionary Algorithm, and how/whether GAs and EAa can be applied successfully to problems of Optimisation, Navigation, and also other contemporary and emerging fields.
This book emphasizes on a lot of fresh ideas (which already requires background in GA, and Algorithms) and may be highly recommended for reference reading of Evolutionary Algorithms and allied Techniques.
could GA get possibly any eaiser to understand???.......2005-03-20
I saw this book once with one of my buddies,and read the first chapter,,,it was after looking up the first chapter i decided to buy it...I have read some other books on this topic,and since i was kinda in rush for a project which needed GA,i found no other book which explains the concepts and procedures, this straightforward and "right to the point".As far as writing this book goes, "Michalewicz" has done a really really great job.
Go for it guys!!!
cheers,
Amir
One of the best book on genetic algorithms.......2002-07-12
A very good vision of the evolutionary optimisation techniques not only GA. As well there is an excellent chapter on constraints handling. Maybe it is not one of the easiest book on GA but it is definitely the most useful.
pretty bad.......2001-06-13
I agree with the previous reviewer: books should be clear and get to the point. Forget about this one. Get Michalewicz and Fogel's "How to solve it" book. It is MUCH better than this one in all levels: it is better written and the content is more authorative and helpful to novices and experts.
This book is supposed to be a textbook. Maybe that's why it sells so well. I guess I am lucky I didn't have to take a class with this thing.
Awful, unreadable book........2000-11-14
This man needs to invest in a good editor. Many times I'd read through half a page or so, stop to think about it and then rephrase it into one or two sentences. Blobs of math appear to be thrown in with little justification, and the book isn't improved by them.
But this book is not only unreadable, it's also not useful. It's more an overview of the area than anything else; it doesn't give adequate information about genetic programming or neural networks. It skims many areas in a close to incomprehensible fashion without covering any in what I would consider to be good detail.
Finally, I'm not dim. I have a PhD myself and am used to ploughing through gibberish. But save your money and don't buy this book (Unless you have a wobbly table that needs fixing).
Average customer rating:
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Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)
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ASIN: 3540290060 |
Book Description
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.
Book Description
This book constitutes the refereed proceedings of the 5th International Workshop on Ant Colony Optimization and Swarm Intelligence, ANTS 2006, held in Brussels, Belgium, in September 2006.
The 27 revised full papers, 23 revised short papers, and 12 extended abstracts presented were carefully reviewed and selected from 115 submissions. The papers are devoted to theoretical and foundational aspects of ant algorithms, evolutionary optimization, ant colony optimization, and swarm intelligence and deal with a broad variety of optimization applications in networking, operations research, multiagent systems, robot systems, networking, etc.
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Genetic Programming: 9th European Conference, EuroGP 2006, Budapest, Hungary, April 10-12, 2006. Proceedings (Lecture Notes in Computer Science)
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ASIN: 3540331433 |
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This book constitutes the refereed proceedings of the 9th European Conference on Genetic Programming, EuroGP 2006, held in Budapest, Hungary, in April 2006, colocated with EvoCOP 2006.
The 21 revised plenary papers and 11 revised poster papers were carefully reviewed and selected from 59 submissions. The papers address fundamental and theoretical issues, along with a wide variety of papers dealing with different application areas, such as computer science, engineering, machine learning, Kolmogorov complexity, biology and computational design, showing that GP is a powerful and practical problem-solving paradigm.
Average customer rating:
- The German Tradition
- The first of its kind
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Theory of Evolution Strategies
Hans-Georg Beyer
Manufacturer: Springer
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ASIN: 3540672974 |
Book Description
Evolutionary Algorithms, in particular Evolution Strategies, Genetic Algorithms, or Evolutionary Programming, have found wide acceptance as robust optimization algorithms in the last ten years. Compared with the broad propagation and the resulting practical prosperity in different scientific fields, the theory has not progressed as much.
This monograph provides the framework and the first steps toward the theoretical analysis of Evolution Strategies (ES). The main emphasis is on understanding the functioning of these probabilistic optimization algorithms in real-valued search spaces by investigating the dynamical properties of some well-established ES algorithms. The book introduces the basic concepts of this analysis, such as progress rate, quality gain, and self-adaptation response, and describes how to calculate these quantities. Based on the analysis, functioning principles are derived, aiming at a qualitative understanding of why and how ES algorithms work.
Customer Reviews:
The German Tradition.......2005-08-21
This monograph is a detailed treatment of a strain of evolutionary computing called "evolution strategies" (ES), which comes out of Germany and follows from the work of Ingo Rechenberg, Hans-Paul Schwefel, Günter Rudolph, Beyer, and a few others. It is distinct from Goldberg's genetic algorithms (GA), Fogel's evolutionary programming (EP), Koza's genetic programming (GP), and simulated annealing.
It is quite a dense book, making heavy use of differential geometry. You can find a more brief treatment of ES along with a comparison to EP and GA in Bäck's "Evolutionary Algorithms in Theory and Practice".
The first of its kind.......2001-09-11
This is the first book that I am aware of that addresses the foundations of evolutionary and genetic algorithms, evolution strategies, and evolutionary programming from a rigorous mathematical point of view. The book is designed for an audience of mathematicians and computer scientists who are curious about evolutionary strategies and need a formal treatment of its foundations. Readers currently involved in designing and writing genetic programs will find this book helpful in the optimizing of their algorithms, even though at times they might find the presentation a little heavy-handed.
Evolutionary strategies are thought of as dynamical systems in the book, but these are not in general deterministic, but probabilistic in nature. The state space of the dynamical system consists of the direct product of an object parameter space, an endogenous strategy parameter set, and a collection of fitness functions. Evolution takes place in this state space via the "genetic operators", i.e. the selection, mutation, reproduction, and recombination operators. The goal of course is to find an optimum solution to the problem, and so a consideration of the convergence of the evolution strategy to this optimum must be addressed.
These issues and others, such as the differentiation between evolutionary strategies and ordinary Monte Carlo methods, are discussed in great detail in the book. The author emphasizes that the mechanism of evolutionary strategies lies in the local properties of state space, the evolutionary process being obtained by small steps in this space. He also suggests three prerequisites for the working of evolutionary algorithms, namely the evolutionary progress principle, the genetic repair hypothesis, and mutation-induced operation by recombination. The first is the statement that each change of the individuals in the state space can result in fitness gain as well as fitness loss. The second is a device employed for statistical estimation, and attempts to answer why recombinant evolution strategies are better than nonrecombinant strategies. The third is the statement that dominant recombination causes cohesion of a population and is represented by a local operator which transforms the mutations by a random sampling process.
The author makes use of differential geometry in the book to establish a theoretical framework to predict the local performance of evolution strategies. The hypersurface model is constructed as a fitness model for the calculation of progress measures, and for an elementary model of evolution dynamics. Tensor calculus is employed to study deformations of the sphere model, with the goal of obtaining useful formulae for the progress rate. A mean radius of this deformation is calculated, to serve as a substitute radius in the progress rate formulae for the sphere model.
For the case of (1+1)-selection, i.e. one parent and one offspring, where both parents and offspring are contained in the selection pool, the author derives exact integral representations for the progress rate. The quality gain for one parent and any member of offspring is also considered, and the author derives an integral expression for it using an approximation of the distribution function of the mutation-induced fitness distribution. He argues that the progress rate and the quality gain are progress measures that describe totally different aspects of the performance of evolution strategies.
The general problem of an evolution strategy with arbitrary numbers of parents and offspring is also considered. Since the distribution of parents in the parameter space is unknow, and since it changes in successive generations, this makes the analysis of the progress rate extremely difficult. The author does however derive the relations for this model in terms of a formal expression for the progress rate which is given as an integral over the distribution of a single descendant, which is generation-dependent and unknown. This distribution is approximated using Hermite polynomials and the determination of this function is then reduced to the finding of a collection of coefficients. These coefficients are functions of moments of the offspring and are estimated by the random selection process of the evolution strategy.
Recombinative evolution strategies are also studied by the author, and two special recombination types considered, namely the intermediate and dominant cases. Intermediate recombination is shown to lead to higher performance compared to nonrecombinativie strategies. The dominant case is shown to lead to mutation-induced speciation by recombination.
The author also analyzes the dynamic adaptation of the mutation strength to the local topology of the fitness landscape. Self-adaptation, which is the method for applying evolution to the adjustment of optimal strategy parameter values, is given detailed treatment for the case of one parent in terms of mean value dynamics.
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Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Swarm Intelligence (The Morgan Kaufmann Series in Artificial Intelligence)
