Multi-Objective Optimization Using Evolutionary Algorithms

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.

Evolutionary Games and Population Dynamics

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The Best There Is On Evolutionary Dynamics

Evolutionary Games and Population Dynamics
Josef Hofbauer , and Karl Sigmund
Manufacturer: Cambridge University Press
ProductGroup: Book
Binding: Paperback

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ASIN: 052162570X

Book Description

Every form of behavior is shaped by trial and error. Such stepwise adaptation can occur through individual learning or through natural selection, the basis of evolution. Since the work of Maynard Smith and others, it has been realized how game theory can model this process. Evolutionary game theory replaces the static solutions of classical game theory by a dynamical approach centered not on the concept of rational players but on the population dynamics of behavioral programs. In this book the authors investigate the nonlinear dynamics of the self-regulation of social and economic behavior, and of the closely related interactions among species in ecological communities. Replicator equations describe how successful strategies spread and thereby create new conditions that can alter the basis of their success, i.e., to enable us to understand the strategic and genetic foundations of the endless chronicle of invasions and extinctions that punctuate evolution. In short, evolutionary game theory describes when to escalate a conflict, how to elicit cooperation, why to expect a balance of the sexes, and how to understand natural selection in mathematical terms.

Customer Reviews:

The Best There Is On Evolutionary Dynamics.......2000-07-14

When I was writing the chapter on evolutionary dynamics for my book Game Theory Evolving (Princeton, 2000), I looked at all the books available and found nothing. Then Hofbauer and Sigmund's new book (a totally revised version of their earlier Theory of Evolution and Dynamical Systems) came out, and I knew I had a masterpiece in hand.

The book does not assume the reader knows basic differential equation theory--it presents all the theory necessary. Indeed, it is a wonderful way to learn differential equation theory, since one immediately is faced with meaningful problems to solve. It does assume the reader is familiar with multivariate calculus. The book should be accessible to biologists and game theorists with a minimum understanding of each other's disciplines.

There are four parts. First, HS deal with Lotka-Volterra equations of the type prevalent in predator-prey models, which they extend to ecological models and several populations. Like the rest of the book, there are lots of problems and the presentation is elegant and succinct.

The second part deals with game theory dynamics and replicator equations, including sections on evolutionary games and asymmetric games. This too is extremely nicely presented, and the links to the Lotka-Volterra models are made clear.

Part three is on dynamical systems especially of relevance to biochemistry--catalytic hypercycles--as well as higher dimensional phase space dynamics of ecological models.

Part four deal with population genetic models using a differential equation approach. This section is also excellent, though for serious readers it should be complemented by Karlin and Taylor's Second Course in Stochastic Processes (which is much more mathematically demanding).

Book Description

All of life is a game and evolution by natural selection is no exception. The evolutionary game theory developed in this book provides the tools necessary for understanding many of nature’s mysteries, including co-evolution, speciation, extinction and the major biological questions regarding fit of form and function, diversity, procession, and the distribution and abundance of life. Mathematics for the evolutionary game are developed based on Darwin's postulates leading to the concept of a fitness generating function (G-function). G-function is a tool that simplifies notation and plays an important role developing Darwinian dynamics that drive natural selection. Natural selection may result in special outcomes such as the evolutionarily stable strategy (ESS). An ESS maximum principle is formulated and its graphical representation as an adaptive landscape illuminates concepts such as adaptation, Fisher’s Fundamental Theorem of Natural Selection, and the nature of life’s evolutionary game.

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All of life is a game and evolution by natural selection is no exception. The evolutionary game theory developed in this book provides the tools necessary for understanding many of nature's mysteries, including co-evolution, speciation, extinction and the major biological questions regarding fit of form and function, diversity, procession, and the distribution and abundance of life. Mathematics for the evolutionary game are developed based on Darwin's postulates leading to the concept of a fitness generating function (G-function). G-function is a tool that simplifies notation and plays an important role developing Darwinian dynamics that drive natural selection. Natural selection may result in special outcomes such as the evolutionarily stable strategy (ESS). An ESS maximum principle is formulated and its graphical representation as an adaptive landscape illuminates concepts such as adaptation, Fisher's Fundamental Theorem of Natural Selection, and the nature of life's evolutionary game.

Customer Reviews:

A little bit disappointing.......2006-04-19

I am not a biologist, but an engineer interested in evolution and mathematics.
The mathematics of the book is very easy, the only (very) confusing issue are the indices.
The G-function is introduced a bit ad-hoc, but as a definition, this might not matter much. It is very clear, that by allowing the strategy to vary, one can get optimal (at least stationary) values. The strategy dynamics are introduced in a rather confusing way, without much of an explanation.

For the rest, it seems, that 80% of the book are numerical examples, which seem to prove mostly, that with nonlinear differential equations, the behaviour of (e.g.) stationary points can vary quite a bit, if the coefficients in those equations are changed.

Maybe a professional biologist gets a lot out of this book, but for the interested layman it offers little (except upteen numerical examples, see above)

Mathematical Darwinism.......2005-11-17

First, full disclosure: I am a colleague and friend of the authors, Thomas L. Vincent and Joel S. Brown, and I reviewed the entire book during its writing.

Game theory is a fairly recent development in mathematics, having been introduced in the 1940's. Evolutionary Game Theory is more recent yet - Maynard Smith and Price put it on the map with their publication in Nature in 1973 on the Logic of Animal Conflict. Maynard Smith then more fully elaborated the application of matrix games to evolution with his 1982 volume, Evolution and the Theory of Games. Vincent and Brown trace their contribution to the pioneering developments of Maynard Smith, but in this volume, they go much further. As I reviewed the eleven chapters as they were first written, I felt the privilege of observing, first hand, the construction of a great edifice. In this edifice, the dynamics of ecology is dovetailed with the dynamics of heritable strategies. The tool that accomplishes this is the fitness generating function, known as the G-function. Particularly brilliant is the invention of the virtual strategy, a scalar or vector "place holder" in the G-function. The great virtue of the virtual strategy is that it represents any focal individual taking on any strategy within the entire strategy set of the species. The fitness generating function then determines the fitness for that virtual strategy within the biotic and abiotic environment defined by the set of arguments (e.g., resident strategies, their population sizes, abundance of resources, etc.) defining the G-function. With G-function in hand, Evolutionary Game Theorists now have a mathematical Darwinism - a formal mathematical expression of Darwin's three postulates: a) like begets like; b) organisms struggle for existence; c) heritable traits help determine the outcome of the struggle. With the G-function, we can predict both the dynamics of heritable strategies and the adaptive outcome of natural selection.

Vincent and Brown begin, in Chapter 1, with an historical and philosophical overview of Evolutionary Game Theory and its relationship to the more traditional approach of Evolutionary Genetics. They then proceed to lay the mathematical foundations (Chapters 2 - 7), constructing the theory of Evolutionary Games and the G-function. These chapters each contain useful examples, teaching the student of evolutionary games how to apply the G-function. Noteworthy is that most all of the examples in these chapters represent continuous, as opposed to matrix games. In matrix games, which constitute the bulk of early development of Evolutionary Game Theory, and with which most readers are probably most familiar, strategies are discrete rather than continuous. However, the continuous games elaborated by Vincent and Brown (and now, many others) are of far more useful application in Evolutionary Ecology. Key contributions here are the precise mathematical definition of Maynard Smith's seminal Evolutionarily Stable Strategy (ESS) in Chapter 6, and the formulation of the ESS Maximum Principle in Chapter 7. This principle establishes the well-recognized properties of the ESS of invasion resistance and convergent stability, but also the fit of form and function - the ESS strategy is an adaptation - it maximizes individual fitness given the circumstances.

Chapter 8, which treats species concepts, speciation, and extinction, is particularly enlightening. Here the G-function shines! Under traditional approaches, a huge chasm, conceptual and methodological, separates microevolution and macroevolution. Vincent and Brown, armed with the G-function, unify the two: Microevolution is repeatable and reversible evolutionary dynamics within a G-function. Macroevolution is the production of novel G-functions. They demonstrate the versatility of the G-function approach to Evolutionary Game Theory in their discussion of three contexts for extinction (which is as integral to evolution as is speciation). Vincent and Brown introduce many key concepts in Chapter 8. Perhaps most important is their strategy species concept, which relies on their definition of the species archetype. They provide a particularly cogent definition of a species that is ecologically keystone (its presence promotes the persistence, in ecological time, of other species in the community), but they also point out that a species can by evolutionarily keystone - when its presence increases the numbers of species at an ESS. Using these developments, Vincent and Brown investigate mechanisms of speciation, including sympatric speciation, allopatric speciation, adaptive radiations, coevolution, Wright's shifting balance theory, and incumbent replacement. They conclude with a tour de force: a concise and brilliant discussion of the Procession of Life. As they aptly demonstrate, with the G-function approach to the Game of Life, theories such as Punctuated Equilibrium, oft cited as a contradiction of Darwinian Evolution, instead result naturally from Darwin's three postulates!

Chapter 9 is perhaps the least exciting chapter, but it serves the utilitarian purpose of melding the matrix approach to Evolutionary Game Theory with the G-function approach. This is, indeed, required reading for those who think matrix games are the only game in town.

Chapters 10 and 11 are well worth the wait and development. In these chapters, Vincent and Brown apply the G-function to an impressive diversity of problems arising in the beautiful metaphor of Hutchinson, the Ecological Theater and Evolutionary Play. Though the diversity of topics covered in these two chapters is impressive, as Vincent and Brown state, it represents only a subset of the problems that can be investigated with G-functions. Chapter 10 addresses "basic" issues of Evolutionary Ecology - a who's who of fundamental subjects. These include: Habitat selection and the ideal free distribution; Consumer-resource games, with examples on plant competition and root-shoot ratio; Carcinogenesis (a must read for all interested in Darwinian Medicine); Flowering time for annual plants; Root competition; and Foraging games.

Chapter 11 turns to the G-function as a fundamental tool for Applied Evolutionary Ecology. Here Vincent and Brown examine: Evolutionary responses to harvesting; Resource management and conservation; and Chemotherapy-driven evolution. They contrast management based on ecological enlightenment with that based on evolutionary enlightenment (prescriptions based on each emphasis are not always identical!). They point out the resemblance of control of a cancer with chemotherapy with control of a population through hunting. The analysis is striking, with the main message that if all cancer cells are not destroyed by a chemotherapy session, the survivors will evolve as the first step of what they call chemotherapy-driven evolution. If ever Evolutionary Ecologists were looking for a raison d'être, here they have it!

Life is a game.......2005-08-29

Evolutionary Game Theory, Natural Selection, and Darwinian Dynamics by Thomas L. Vincent and Joel S. Brown is a book that not only belongs among the classics of evolutionary theory, but should have pride of place on the shelf right after Darwin's Origin of Species and Maynard Smith's Evolution and the Theory of Games.

This book makes a novel, interesting and readable contribution to the proper understanding of Darwinian processes in evolution. Based on more than twenty years of collaboration between the authors, the book is a comprehensive review of Darwinian theory newly cast in an over-arching mathematical framework. Unlike Stephen Jay Gould's recent overview of evolutionary theory (The Structure of Evolutionary Theory, 2002, 1433 pages), Vincent & Brown's book is concise (382 pages), uncluttered, and supported by an elegant skeleton of mathematical theory.

Don't let the math dissuade you however. If you have read Origin of Species and have a familiarity with classic evolutionary games, you won't have trouble understanding this book. Text and numerous examples provide a clear conceptual explanation of equations throughout.

The book's premise is that life is a game and its players have strategies. Understood as such, the authors present fitness-generating functions (G-functions) that encompass strategy, population, and Darwinian dynamics to model evolutionary outcomes. The first chapter introduces this philosophy; the next six chapters develop the theory, presenting classic population models (Ch. 2) and evolutionary games (Ch. 3), then forging new theory through deriving G-functions (Ch. 4), modeling Darwinian dynamics (Ch. 5), finding the evolutionary stable strategies (ESS, Ch. 6) and developing their general ESS maximum principle (Ch. 7).

The authors are able to side-step population-genetics models (and notably, are able to explain WHY this is possible), and build a general model of Darwinian evolution. An immediate insight of their general model is the concept of flexible landscapes, which re-envisions the notion that natural selection cannot cross valleys on evolutionary landscapes, one of the fundamental criticisms of Darwinian theory since the New Synthesis. Exploration of Vincent & Brown's model illustrates that flexible landscapes can shift under evolving populations so that "valleys" are spanned by continuously uphill routes, re-forming behind evolving populations after they have passed. Further, Vincent & Brown derive the general conditions where flexible landscapes will or will not occur (frequency-dependent vs. -independent evolution respectively).

Armed with their general theory, Vincent & Brown are not content to stop after illuminating the valley conundrum, however, and go on in subsequent chapters to apply their theory to classic problems in evolution (Ch. 8; sympatric and allopatric speciation, co-evolution, the difference between micro- and macro-evolution) and ecology (Ch. 9 & 10; sex ratios, cooperation, ideal free distribution, consumer-resource competition), and even medicine (Ch. 10; the ontogenesis of cancer, chemotherapy) and ecosystem management (Ch. 11, evolutionary stable and ecologically enlightened resource management).

In short, Vincent and Brown have written a marvelous book; and from the day it was published, any evolutionary scholar who has not read it has been behind in the field, and has some catching up to do. It should also be read by ecologists, behaviorists, medical researchers and resource managers interested in evolutionary aspects of their work.

A Mathematical Approach to Evolution.......2005-08-03

Charles Darwin published his primary thesis 'The Origin of Species' in 1859. It was a masterpiece of logical deduction based on the observations he had made while serving as a naturalist aboard the H.M.S. Beagle on a scientific expedition around the world. His views were both orthodox for the day and flawed.

Only seven years later Mendel published the results of his research on genetics. Over time these sciences were merged together into what is now called the 'Modern Synthesis.' Genetics explains the why and the how of species begetting species, and how changes in the species are made when a change is made in the genes.

In 1944, with the advances in mathematics, von Neumann and Morgenstern published 'Theory of Games and Economic Behavior.' Over time the modern synthesis of the genertic approach to evolution has been fit into game theory to help understand how the randomness of genetic evolution can be predicted using game theory.

This book gives a rigorous introduction to the mathematics of game theory as applied to Natural Selection. The book presents the tools necessary for understanding many of Nature's mysteries.

Evolutionary Optimization (International Series in Operations Research & Management Science)

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EAs for Optimization

Evolutionary Optimization (International Series in Operations Research & Management Science)

Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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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.

Foundations of Complex-system Theories: In Economics, Evolutionary Biology, and Statistical Physics

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4.5 Stars-The whole is not the sum of the parts;Excellent and scholarly
a fascinating book -- recommended to philosophers
A Professional work

Foundations of Complex-system Theories: In Economics, Evolutionary Biology, and Statistical Physics
Sunny Y. Auyang , and Sunny A. Auyang
Manufacturer: Cambridge University Press
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Binding: Paperback

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ASIN: 0521778263

Book Description

Complex behavior can occur in any system made up of large numbers of interacting constituents, be they atoms in a solid, cells in a living organism, or consumers in a national economy. Analysis of this behavior often involves making important assumptions and approximations, the exact nature of which vary from subject to subject. Foundations of Complex-system Theories begins with a description of the general features of complexity and then examines a range of important concepts, such as theories of composite systems, collective phenomena, emergent properties, and stochastic processes. Each topic is discussed with reference to the fields of statistical physics, evolutionary biology, and economics, thereby highlighting recurrent themes in the study of complex systems. This detailed yet nontechnical book will appeal to anyone who wants to know more about complex systems and their behavior. It will also be of great interest to specialists studying complexity in the physical, biological, and social sciences.

Customer Reviews:

4.5 Stars-The whole is not the sum of the parts;Excellent and scholarly.......2006-02-14

This is a very interesting book.The author demonstrates that she has command over a number of different fields.She exhibits a wide ranging scholarship in this book.In a nutshell,one can categorize the major conclusions she arrives at as the whole is not the sum of the parts alone.Neither a strictly micro or macro approach to the different fields she investigates,using a complex systems framework, yields the idealized types of scientific discovery and knowledge one finds postulated in some philosophy of science discourses that emphasize deductive closure laws.I have one slight criticism of the book,which is why I have subtracted one half a star.The author has a deep general understanding of the Keynes-Knight distinction between risk and uncertainty in economics(and in social sciences).However,she lacks an understanding of the specifics of Keynes's approach in the A Treatise on Probability(1921;TP).She is unaware of Keynes's interval estimate approach to probability,his index,w,used to measure the completeness of the evidence ,ranging from ignorance through partial knowledge to a complete information set,and Keynes's conventional coefficient of weight and risk,which treats risk, based on the purely deductive laws of probability, as a special case.This would be a very minor criticism if she had integrated the work of D.Ellsberg(Ellsberg's 2001 book,Risk,Ambiguity,and Decision gives a modern,improved and updated version of the TP) and B.Mandelbrot into her discussions involving economics,risk,and uncertainty(Ellsberg's Ambiguity with his rho and alpha indexes and the wild versus mild risk of the cauchy distribution versus normal distribution as discussed by Mandelbrot).Unfortunately,Ellsberg's contributions are not discussed at all while Mandelbrot receives a single footnote that completely ignores his contributions to economics.She can certainly obtain a 5-star rating by bringing out a revised edition in which the original,technical, pioneering work of Keynes is covered followed by the modern and updated contributions of Ellsberg and Mandelbrot.

a fascinating book -- recommended to philosophers.......2002-10-26

Philosophers of science need to read this book: the hands-on
account of how three sciences work is a healthy
corrective to the usual practice of writing philosophy of science
without actually knowing how the science is done.

A Professional work.......2000-03-30

This is an amazing work. Sunny Auyang has written an easily comprehenedible book on applications of complexity theories to economics, biology and physics. It is a professional writing to professionals in different fields.One needs college level maths and some physics to fully grasp it but she has made minimum use of mathematical symbols. Her writing flows, the examples are clear, some illuminate important issues in the applied fields, some are just homey bits that convey an idea insightfully. A lot of depth in her philosophical explorations of the complexity ideas. I consider this to be a must for any person studying or instructing in system thinking.

Evolutionary Computation: Principles and Practice for Signal Processing (SPIE Tutorial Texts in Optical Engineering Vol. TT43)

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Unique contribution for signal processing

Evolutionary Computation: Principles and Practice for Signal Processing (SPIE Tutorial Texts in Optical Engineering Vol. TT43)
David B. Fogel
Manufacturer: SPIE Publications
ProductGroup: Book
Binding: Paperback

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ASIN: 0819437255

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Evolutionary computation is one of the fastest growing areas of computer science, partly because of its broad applicability to engineering problems. The methods can be applied to problems as diverse as supply-chain optimization, routing and planning, task assignment, pharmaceutical design, interactive gaming, and many others within the signal processing domain. The book is an outgrowth of successful SPIE short courses taught by the author. The examples span a range of applications and should be useful to a variety of readers with different backgrounds and expertise.

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Unique contribution for signal processing.......2000-10-16

The book is excellent. It provides a comprehensive description of evolutionary computation applied to almost every facet of signal processing: classification, clustering, time series, system identification, etc. The final chapter provides some real insight on how to design evolutionary algorithms that is very helpful. The material is well presented and easy to understand. I really like it and the cost is reasonable.

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A must read...only for the serious game theorists, though.
Hard to read and to apply
It explains Evolutionary Game Theory very well
Not much usefull for practical purposes

Evolutionary Game Theory
Jörgen W. Weibull
Manufacturer: The MIT Press
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Binding: Hardcover

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ASIN: 0262231816

Book Description

This text introduces current evolutionary game theory -- where ideas from evolutionary biology and rationalistic economics meet -- emphasizing the links between static and dynamic approaches and noncooperative game theory. Much of the text is devoted to the key concepts of evolutionary stability and replicator dynamics. The former highlights the role of mutations and the latter the mechanisms of selection. Moreover, set-valued static and dynamic stability concepts, as well as processes of social evolution, are discussed. Separate background chapters are devoted to noncooperative game theory and the theory of ordinary differential equations. There are examples throughout as well as individual chapter summaries.

Because evolutionary game theory is a fast-moving field that is itself branching out and rapidly evolving, Jörgen Weibull has judiciously focused on clarifying and explaining core elements of the theory in an up-to-date, comprehensive, and self-contained treatment. The result is a text for second-year graduate students in economic theory, other social sciences, and evolutionary biology. The book goes beyond filling the gap between texts by Maynard-Smith and Hofbauer and Sigmund that are currently being used in the field.

Evolutionary Game Theory will also serve as an introduction for those embarking on research in this area as well as a reference for those already familiar with the field. Weibull provides an overview of the developments that have taken place in this branch of game theory, discusses the mathematical tools needed to understand the area, describes both the motivation and intuition for the concepts involved, and explains why and how it is relevant to economics.

Customer Reviews:

A must read...only for the serious game theorists, though........2006-07-15

Weibull's "Evolutionary Game Theory" has earned a distinguished place in many bookshelves for good reason: It is rigorous and never short of intuition. That said, however, this book is not the first item in the reading list of a beginner.

If you are interested in learning evolutionary game theory and your previous exposure to non-cooperative game theory and ordinary differential equations has been limited, do not start with Weibull's Evolutionary Game Theory. Consider first visiting Herbert Gintis's "Game Theory Evolving" and Maynard Smith's classic "Evolution and the Theory of Games"

For the 'technical' reader this book still is not a walk in the park becasue Weibull walks the reader not only in a math garden but also exposes the reader to several important evolutionary concepts including but not limited to 'evolutionary stability','evolutionarily stable strategy', 'replicator dynamics', 'population dynamics'. Grasping both the theoretical concepts and how they are modelled takes some thinking and patience.

Overall this is a must reader for the seriously involved and can be the single item for many students of this subject that takes them to a higher plane of understanding.

Hard to read and to apply.......2005-09-09

I'm a computer sciences engineer working on my phd thesis that is related with game thoery. I found the book difficult to read. Forget about following an entire chapter if you are weak on differential equations.

It explains Evolutionary Game Theory very well.......2005-07-17

After one makes it through umpteen refinements of Nash equilibria, the book becomes fascinating. Many ideas of Darwinism became much clearer -they got a quality of unavoidability so to speak- than when I read books on Darwinism before.
I found the level of mathematical sophistication needed rather unchallenging, without being boring - and I am not a "deep core" mathematician, but an engineer.

Highly recommendable

Not much usefull for practical purposes.......2000-07-27

During the work on my master thesis ("Learning in strategic games") i bought several books about the topic. This one was the hardest to understand and to apply to anything practical. I guess this one is for "hard core" mathematicians.

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The German Tradition
The first of its kind

Theory of Evolution Strategies
Hans-Georg Beyer
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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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.

Book Description

This book presents a unified view of evolutionary algorithms: the exciting new probabilistic search tools inspired by biological models that have immense potential as practical problem-solvers in a wide variety of settings, academic, commercial, and industrial. In this work, the author compares the three most prominent representatives of evolutionary algorithms: genetic algorithms, evolution strategies, and evolutionary programming. The algorithms are presented within a unified framework, thereby clarifying the similarities and differences of these methods. The author also presents new results regarding the role of mutation and selection in genetic algorithms, showing how mutation seems to be much more important for the performance of genetic algorithms than usually assumed. The interaction of selection and mutation, and the impact of the binary code are further topics of interest. Some of the theoretical results are also confirmed by performing an experiment in meta-evolution on a parallel computer. The meta-algorithm used in this experiment combines components from evolution strategies and genetic algorithms to yield a hybrid capable of handling mixed integer optimization problems. As a detailed description of the algorithms, with practical guidelines for usage and implementation, this work will interest a wide range of researchers in computer science and engineering disciplines, as well as graduate students in these fields.

Customer Reviews:

scholarly and from a general viewpoint.......2005-07-19

In comparing this book with, say Goldberg's "Genetic Algorithms..." (may be the most popular genetic algorithms text), this book reads more like a German habilitation thesis (which I imagine it may have served as such), where as Goldberg's book seems more of a light introduction for the mathematically uninitiated. Indeed, Back's book seems quite scholarly with lots of useful references, and gives a good introduction to not only genetic algorithms, but also to evolutionary strategies (a paradigm that is most applicable to Euclidean-type search spaces) and evolutionary programming
(simular to ES and not to be confused with genetic programming).

I found Chapters 1 and 2 quite good, in that Chapter 1 presented the biological motivations for evolutionary computing along with a brief introduction to the theory of computation and computational complexity, while Chapter 2 gave a very good introduction to the above-mentioned evolutionary computing paradigms. The remainder of the book reads more like a report on the author's experiments in evolutionary computing.

It is important to note that Goldberg's book does not cover Evolutionary Strategies, which I have found to be a more fruitful approach since it is specifically designed for Euclidean space where many if not most interesting optimization problems are formulated in.

Finally, I offer bit of advice for those who plan to read through this book. Some of the definitions are stated with such generality that they seem very opaque upon first reading. It is very important to understand them, so do not give up! Once the defintions are understood, the algorithms will seem much easier to comprehend. In fact, the algorithms have a very simple outline:
i) initialize population
ii) while the terminating condition is not yet met: recombine to form new population members, mutate the population members, select the most fit population members to form the next generation.
The partial analyses provided for the algorithms can be skipped on first reading.

One of the best books on EAs.......2003-10-23

Although this book is much less popular than Goldberg's and Mitchell's, it is the most complete reference on evolutionary algorithms in my opinion. If you're looking only for an introduction to EAs, this may not be the perfect book for you (the 2 other ones are more concise) but if you're seeking a detailed review of foundations of EAs, this book is excellent. It provides mathematical insight, and examples of how to implement such algorithms.

One of the best introductions to evolutionary algorithms.......2001-08-01

I don't really know why this book didn't sell as well as some of the other standard books in evolutionary algorithms. It's much better in many respects and presents a balanced view of the entire field, including evolution strategies, evolutionary programming, and genetic algorithms. Anyone who is interested in evolutionary algorithms should have this book....

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specialised maths treatment of GP
A survey of what was new in 2002
The modern revolution
Exciting New Developments in EC Theory
Good introduction to GP theory

Foundations of Genetic Programming
William B. Langdon , and Riccardo Poli
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 3540424512

Book Description

Genetic programming (GP), one of the most advanced forms of evolutionary computation, has been highly successful as a technique for getting computers to automatically solve problems without having to tell them explicitly how. Since its inceptions more than ten years ago, GP has been used to solve practical problems in a variety of application fields. Along with this ad-hoc engineering approaches interest increased in how and why GP works. This book provides a coherent consolidation of recent work on the theoretical foundations of GP. A concise introduction to GP and genetic algorithms (GA) is followed by a discussion of fitness landscapes and other theoretical approaches to natural and artificial evolution. Having surveyed early approaches to GP theory it presents new exact schema analysis, showing that it applies to GP as well as to the simpler GAs. New results on the potentially infinite number of possible programs are followed by two chapters applying these new techniques.

Customer Reviews:

specialised maths treatment of GP.......2006-04-04

This book can be usefully read along with a companion text by the same publisher - "Introduction to Evolutionary Computing". Langdon and Poli provide a focused look, on the specifics of genetic programming. The maths treatment here is significantly more involved than the other book.

Foundations starts with what I suppose in this field is an obligatory section on the concept of a fitness landscape. A very useful metaphor of what you'll be attempting to do, as a researcher. However, the authors carefully point out the limitations of this idea. Notably that some spaces might have no natural metric.

The book then rapidly goes into the ideas of GP schemas and hyperschemas. Accompanied by a nice theoretical analysis of key performance goals like the rate of convergence in the GP search space. A solid offering to the GP researcher.

A survey of what was new in 2002.......2004-04-09

This book was published in 2002 to provide a survey of the direction research had taken in the field of Genetic Programming. There is an explanation of what genetic programming is and how it is different from genetic algorithms in chapter 1(GP is a "generalization" of GA). Chapter 2 discusses the problems with the fitness landscape. Chapter 3 - 6 discusses various schema theory approaches and proofs. Chapter 6 has a great explanation of effective fitness.

There are numerous theorems and proofs in the book. There are informative examples of the max problem and the artificial ant (Santa Fe Trail) problems. Chapter 11 is about how GP convergences are a tricky matter and how subtrees can hide interesting incidences of convergence.

This is not an introductory text, it is intended for graduate level or higher readers. There is much theoretical work here and a limited background in this area will result in limited understanding of the material.

The modern revolution.......2003-02-18

Currently working as an undergraduate student in Ann Arbor, Michigan as a Computer Science major I'm an intrigued by Genetic Programming alongside all motives of this in-depth field. I found this book to be a modest account of what is new and theoretical within this field. Expressing advanced features with a short introduction; this book is profoundly for somebody with somewhat of a background. A recommended start in the computer evolutionary field is:
An Introduction to Genetic Algorithms [1996], by Melanie Mitchell.

Exciting New Developments in EC Theory.......2002-09-20

Langdon and Poli are both internationally recognized experts in Evolutionary Computation (EC) and, in particular, Genetic Programming. They have both contributed extensively to the theoretical "foundations" of GP and hence may speak with no small degree of authority about GP theory. As a physicist working in EC I like the balance that the authors have struck between mathematical rigor and understandable intuition. The book is not as rigorous as Vose's well known GA book. However, it is much easier to read. Neither does it take the "engineering" rule of thumb approach, as does Goldberg's book for instance. It covers very well recent important developments in the theory of GP and in that sense makes very good reading for anyone with a serious interest in EC theory. It is not for the novice, even though technically it is not a difficult book. It is really a research monograph and not a textbook. In that sense the title is a little bit misplaced. With the exciting direction the authors are pointing in I believe that in five years time another book of the same title should truly be able to lay out what are the foundations of GP theory and also show the theoretical unity that exists between the different branches of EC.

Good introduction to GP theory.......2002-08-25

Langdon and Poli do a fantastic job of summarizing the major theoretical results of genetic programming. The first chapter gives a quick and clear introduction to genetic programming. They continue with a comprehensive summary of previous research in schema theory, and then they present their exciting theoretical results. Their description of an exact schema theorem (microscopic and macroscopic) for GP is a bit dense, but they provide a good discussion of how to interpret these results. As a whole, this book is generally easy to follow, even with little prior exposure to genetic programming. Of course, this book is not intended to be a general introduction to genetic programming (one of John Koza's books would be more appropriate), but instead it is intended to present some of the theoretical foundations of the field.

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