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.

Average customer rating:

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
ProductGroup: Book
Binding: Paperback

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ASIN: 0262731215

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. The author 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 the theory 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.

Individual Strategy and Social Structure

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only one word: excellent!

Individual Strategy and Social Structure
H. Peyton Young
Manufacturer: Princeton University Press
ProductGroup: Book
Binding: Hardcover

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ASIN: 069102684X

Book Description

Neoclassical economics as-sumes that people are highly rational and can reason their way through even the most complex economic problems. In Individual Strategy and Social Structure, Peyton Young argues for a more realistic view in which people have a limited understanding of their environment, are sometimes short-sighted, and occasionally act in perverse ways. He shows how the cumulative experiences of many such individuals coalesce over time into customs, norms, and institutions that govern economic and social life. He develops a theory that predicts how such institutions evolve and characterizes their welfare properties.

The ideas are illustrated through a variety of examples, including patterns of residential segregation, rules of the road, claims on property, forms of economic contracts, and norms of equity. The book relies on new results in evolutionary game theory and stochastic dynamical systems theory, many of them originated by the author. It can serve as an introductory text, or be read on its own as a contribution to the study of economic and social institutions.

Customer Reviews:

only one word: excellent!.......1999-08-03

A wonderful survey of the author's contributions to evolutionary games and their implications for institutional changes. The reader will learn from this book how random perturbation theory of Freidlin and Wentzell is applied to such an increasingly important field in both game theory and macroeconomics. I like chapter 6 "Local Interaction" the most and, of course, the other chapters are also great!

Evolution, Games, and Economic Behaviour

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Evolution, Games, and Economic Behaviour

Manufacturer: Oxford University Press, USA
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Binding: Paperback

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Economics and the Theory of Games

ASIN: 0198774729

Book Description

This textbook for advanced undergraduate and postgraduate students of Evolutionary Game Theory covers recent developments in the field, with an emphasis on economic contexts and applications. It begind with the basic ideas as they originated within the field of theoretical biology and then proceeds to the formulation of a theoretical framework that is suitable for the study of social and economic phenomena from an evolutionary perspective. Core topics include the Evolutionary Stable Strategy (EES) and Replicator Dynamics (RD), deterministic dynamic models, and stochastic perturbations. A set of short appendices presents some of the technical material referred to in the main text. Evolutionary theory is widely viewed as one of the most promising appraoches to understanding bounded rationality, learning, and change in complex social environments. New avenues of research are suggested by Vega-Redondo, and plentiful examples illustrate the theory's potential applications. The recent boom experienced by this discipline makes the book's systematic presentation of its essential contributions vital reading for any newcomer to the field.

Advances in Dynamic Game Theory: Numerical Methods, Algorithms, and Applications to Ecology and Economics (Annals of the International Society of Dynamic Games)

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Advances in Dynamic Game Theory: Numerical Methods, Algorithms, and Applications to Ecology and Economics (Annals of the International Society of Dynamic Games)

Manufacturer: Birkhäuser Boston
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Binding: Hardcover

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ASIN: 0817643990

Book Description

This collection of selected contributions gives an account of recent developments in dynamic game theory and its applications, covering both theoretical advances and new applications of dynamic games in such areas as pursuit-evasion games, ecology, and economics. Written by experts in their respective disciplines, the chapters are an outgrowth of presentations from the 11th International Symposium on Dynamic Games and Applications.

Key topics covered include:

* stochastic and differential games

* dynamic games and their applications in various areas, such as ecology and economics

* numerical methods and algorithms in dynamic games

* zero- and nonzero-sum games

* pursuit-evasion games

* evolutionary game theory and applications

The work will serve as a state-of-the art account of recent advances in dynamic game theory and its applications for researchers, practitioners, and advanced students in applied mathematics, mathematical finance, and engineering.

Evolutionary Dynamics and Extensive Form Games (Economic Learning and Social Evolution)

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Evolutionary Dynamics and Extensive Form Games (Economic Learning and Social Evolution)
Ross Cressman
Manufacturer: The MIT Press
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Binding: Hardcover

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ASIN: 0262033054

Book Description

Evolutionary game theory attempts to predict individual behavior (whether of humans or other species) when interactions between individuals are modeled as a noncooperative game. Most dynamic analyses of evolutionary games are based on their normal forms, despite the fact that many interesting games are specified more naturally through their extensive forms. Because every extensive form game has a normal form representation, some theorists hold that the best way to analyze an extensive form game is simply to ignore the extensive form structure and study the game in its normal form representation. This book rejects that suggestion, arguing that a game's normal form representation often omits essential information from the perspective of dynamic evolutionary game theory.

The book offers a synthesis of current knowledge about extensive form games from an evolutionary perspective, emphasizing connections between the extensive form representation and dynamic models that traditionally have been applied to biological and economic phenomena. It develops a general theory to analyze dynamically arbitrary extensive form games and applies this theory to a range of examples. It lays the foundation for the analysis of specific extensive form models of behavior and for the further theoretical study of extensive form evolutionary games.

Active Processor Scheduling Using Evolutionary Algorithms

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Active Processor Scheduling Using Evolutionary Algorithms

Manufacturer: Storming Media
ProductGroup: Book
Binding: Spiral-bound

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ASIN: 1423504186

Product Description

This is a AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH SCHOOL OF ENGINEERING AND MANAGEMENT report procured by the Pentagon and made available for public release. It has been reproduced in the best form available to the Pentagon. It is not spiral-bound, but rather assembled with Velobinding in a soft, white linen cover. The Storming Media report number is A746314. The abstract provided by the Pentagon follows: The allocation of processes to processors has long been of interest to engineers. The processor allocation problem considered here assigns multiple applications onto a computing system. With this algorithm researchers could more efficiently examine real-time sensor data like that used by United States Air Force digital signal processing efforts or real-time aerosol hazard detection as examined by the Department of Homeland Security. Different choices for the design of a load balancing algorithm are examined in both the problem and algorithm domains. Evolutionary algorithms are used to find near-optimal solutions. These algorithms incorporate multiobjective coevolutionary and parallel principles to create an effective and efficient algorithm for real- world allocation problems. Three evolutionary algorithms (EA) are developed. The primary algorithm generates a solution to the processor allocation problem. This allocation EA is capable of evaluating objectives in both an aggregate single objective and a Pareto multiobjective manner. The other two EAs are designed for fine turning returned allocation EA solutions. One coevolutionary algorithm is used to optimize the parameters of the allocation algorithm. This meta-EA is parallelized using a coarse-grain approach to improve performance. Experiments are conducted that validate the improved effectiveness of the parallelized algorithm. Pareto multiobjective approach is used to optimize both effectiveness and efficiency objectives.

Equity, Efficiency and Evolutionary Stability in Bargaining Games with Joint Production (Lecture Notes in Economics and Mathematical Systems)

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Equity, Efficiency and Evolutionary Stability in Bargaining Games with Joint Production (Lecture Notes in Economics and Mathematical Systems)
Manfred Königstein
Manufacturer: Springer
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Binding: Paperback

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The book reports experimental studies and a theoretical investigation of non-cooperative bargaining games with joint production. Such games have rarely been studied within laboratory experiments despite being more general and more natural than bargaining without production. It is shown that equity theory is a good predictor of subjects' behavior. Furthermore subjects exhibit different equity notions. One chapter addresses problems of statistical data analysis that are specific to experiments. Applying evolutionary game theory within a model of bargaining with production it is shown theoretically that altruistic preferences, which generate moderate bargaining behavior, can survive the process of evolution.

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