Genetic Algorithms and Simulated Annealing (Research Notes in Artificial Intelligence,)

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.

Modeling Nature (Science and Its Conceptual Foundations series)

Average customer rating:

An excellent starting point...
scientists are human, science is not ahistorical...

Modeling Nature (Science and Its Conceptual Foundations series)
Sharon E. Kingsland
Manufacturer: University Of Chicago Press
ProductGroup: Book
Binding: Paperback

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ASIN: 0226437280

Book Description

The first history of population ecology traces two generations of science and scientists from the opening of the twentieth century through 1970. Kingsland chronicles the careers of key figures and the field's theoretical, empirical, and institutional development, with special attention to tensions between the descriptive studies of field biologists and later mathematical models. This second edition includes a new afterword that brings the book up to date, with special attention to the rise of "the new natural history" and debates about ecology's future as a large-scale scientific enterprise.

Customer Reviews:

An excellent starting point..........2002-12-27

...for anyone interested in the history and development of Population Biology. It is a real shame that this book has gone out of print (and that Kingsland hasn't chosen to do a second edition)as this relatively short (267pp) book captured the really important trends and ideas of mathematical ecology up to the early 1970's in straight-forward and remarkably non-technical language. Kingsland gives us both the theories and the background and personalities that generated the theories, along with some delightful portraits of the Heavy Hitters during this seminal period in theoretical ecology. She ends with MacArthur & one would like to think that enough has happened since then that a sequel is in order, but I would recommend this book to any advanced undergrad or first-year grad student looking for background material.

scientists are human, science is not ahistorical..........2002-06-23

Kingsland is biology by training therefore many scientific concepts in this book are very welled summarized and organized, making it easy for professionals and non-professional as well to grasp the general ideas in population biology. However, this book focus more on the historical context and the personality of some key scientists in this area, which gives readers more indepth understanding outside science itself. The auther did a wonderful job in interweaving science and human sides, and made it easy to pick up some major transitions in the history of population biology. highly recommmended to professional population ecologists, and general public who is interested in science as well! a bonus: some pictures of famous scientists in this area, such as McArthur, Lokta, Volttera etc...it's interesting to me, after reading all their work, finally had a chance to see what they look like.

Adaptive Learning by Genetic Algorithms: Analytical Results and Applications to Economical Models (Lecture Notes in Economics and Mathematical Systems)

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Adaptive Learning by Genetic Algorithms: Analytical Results and Applications to Economical Models (Lecture Notes in Economics and Mathematical Systems)
Herbert Dawid
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Foundations of Generic Optimization, Volume 2: Applications of Fuzzy Control, Genetic Algorithms and Neural Networks (Mathematical Modelling: Theory and Applications)

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Foundations of Generic Optimization, Volume 2: Applications of Fuzzy Control, Genetic Algorithms and Neural Networks (Mathematical Modelling: Theory and Applications)

Manufacturer: Springer
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Binding: Hardcover

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Book Description

This is a comprehensive overview of the basics of fuzzy control, which also brings together some recent research results in soft computing, in particular fuzzy logic using genetic algorithms and neural networks.

This book offers researchers not only a solid background but also a snapshot of the current state of the art in this field.

Foundations of Generic Optimization: Volume 1: A Combinatorial Approach to Epistasis (Mathematical Modelling: Theory and Applications)

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Foundations of Generic Optimization: Volume 1: A Combinatorial Approach to Epistasis (Mathematical Modelling: Theory and Applications)
M. Iglesias , B. Naudts , and C. Vidal
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ASIN: 1402036663

Book Description

The success of a genetic algorithm when applied to an optimization problem depends upon several features present or absent in the problem to be solved, including the quality of the encoding of data, the geometric structure of the search space, deception or epistasis. This book deals essentially with the latter notion, presenting for the first time a complete state-of-the-art research on this notion, in a structured completely self-contained and methodical way.

In particular, it contains a refresher on the linear algebra used in the text as well as an elementary introductory chapter on genetic algorithms aimed at readers unacquainted with this notion.

In this way, the monograph aims to serve a broad audience consisting of graduate and advanced undergraduate students in mathematics and computer science, as well as researchers working in the domains of optimization, artificial intelligence, theoretical computer science, combinatorics and evolutionary algorithms.

Foundations of Genetic Algorithms 6 (FOGA-6) (The Morgan Kaufmann Series in Artificial Intelligence)

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Foundations of Genetic Algorithms 6 (FOGA-6) (The Morgan Kaufmann Series in Artificial Intelligence)
Worth Martin
Manufacturer: Morgan Kaufmann
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Binding: Hardcover

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ASIN: 155860734X

Book Description

Foundations of Genetic Algorithms, Volume 6 is the latest in a series of books that records the prestigious Foundations of Genetic Algorithms Workshops, sponsored and organised by the International Society of Genetic Algorithms specifically to address theoretical publications on genetic algorithms and classifier systems.

Genetic algorithms are one of the more successful machine learning methods. Based on the metaphor of natural evolution, a genetic algorithm searches the available information in any given task and seeks the optimum solution by replacing weaker populations with stronger ones.

Includes research from academia, government laboratories, and industry
Contains high calibre papers which have been extensively reviewed
Continues the tradition of presenting not only current theoretical work but also issues that could shape future research in the field
Ideal for researchers in machine learning, specifically those involved with evolutionary computation

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Foundations of Mathematical Genetics
Anthony William Fairbank Edwards
Manufacturer: Cambridge University Press
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Binding: Hardcover

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Book Description

In this second edition of the classic work Foundations of Mathematical Genetics, a definitive account is given of the basic models of population genetics together with the historical origins of its development since 1908. This book satisfies the need for a more careful study of the foundations of mathematical population genetics, treating the simple deterministic models for random-mating diploid populations in depth without sacrificing clarity of expression. In the second edition, coverage has been extended with the provision of a new chapter on the Fundamental Theorem of Natural Selection. This book is written for those interested in the mathematical aspects of genetics, ecology, and biology. Students and historians of mathematical genetics will find this work a definitive statement of the origins of modern mathematical population genetics.

Foundations of Mathematical Genetics 2nd Edition

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Foundations of Mathematical Genetics 2nd Edition
Anthony W.F. Edwards
Manufacturer: Cambridge University Press
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The Simple Genetic Algorithm: Foundations and Theory (Complex Adaptive Systems)

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one of a kind modern classic
A mathematical introduction to Genetic Algorithms
A Great Starting Point for GA Research
dense but informative
One person from Galicia

The Simple Genetic Algorithm: Foundations and Theory (Complex Adaptive Systems)
Michael D. Vose
Manufacturer: The MIT Press
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Binding: Hardcover

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ASIN: 026222058X

Amazon.com

It might be simple, but it's not easy. Computer scientist Michael D. Vose takes a rigorous look at The Simple Genetic Algorithm and shows the state of our knowledge in a book appropriate for advanced undergraduates, graduate students, and professionals.

Vose has decided to approach his subject as a mathematical object, keeping his discussion to a minimum and relying on mathematical demonstrations of what has been proven about this powerful genetic search. This approach maximizes the book's utility for its scope of readers; since each chapter builds on the material before, it makes a good teaching tool, but it is still a useful reference as the indexing helps the professional find proofs quickly.

Covering the basics of random heuristic searching and the nature of the algorithm, the book moves on to computing, transient and asymptotic behavior, models, and schemata. Cutting all of the material down to the basic provable theorems is not, as Vose admits, without problems: any speculation beyond these stripped-down proofs is left to the imaginative reader. But the intrepid explorer couldn't ask for firmer ground from which to launch flights of discovery, and genetic computation currently offers the widest frontiers. --Rob Lightner

Book Description

The Simple Genetic Algorithm (SGA) is a classical form of genetic search. Viewing the SGA as a mathematical object, Michael D. Vose provides an introduction to what is known (i.e., proven) about the theory of the SGA. He also makes available algorithms for the computation of mathematical objects related to the SGA. Although he describes the SGA in terms of heuristic search, the book is not about search or optimization per se. Rather, the focus is on the SGA as an evolutionary system. The author intends the book also to serve as an outline for exploring topics in mathematics and computer science in a goal-oriented way.

Customer Reviews:

one of a kind modern classic.......2003-04-25

This book is the result of the author's attempts to "really understand" evolutionary algorithms. It's very mathematical and rigorous, though sometimes the formulation is not very usual. ( a warning!) You may need a few references, and pondering.

Is this a perfect book? Maybe not. But it's very important for the deeper understanding of GA...a landmark great job!

All people who are interested in the underpinning of GA should get this book. It's also a good supplement for mathematical modeling in the sense that it presents a very hard topic that few people have tried to formulate. I mean a very good demonstration of modeling complicated structures like heuristic learning process. And also a good supplement of general dynamical systems.

The style is kind of Dirac-like -- few words, short, original but you barely can add more words to the margins. It's a kind of modernized Chinese meal -- less oily, but still nutritious!! I cannot find any annoying and useless aside in the whole book.

The book is beautiful and well-bound, and nice paper, cover, etc. I got the hardback, though.

A mathematical introduction to Genetic Algorithms.......2002-05-25

This book is for mathematicians or people who want to study genetic algorithms formally. If you are looking for a book that does not emphasize on the mathematical aspects and talks about parallels between genetic algorithms and natural selection,etc., then you should buy the books written by Goldberg or Mitchell.

It is a great introduction to genetic algorithms for advanced undergraduate mathematics students or people with sufficient math knowledge and maturity. If you read it without these prerequisites, you will only be able to understand little bits and will get lost in the formalism.

Prior knowledge of genetic algorithms is recommended. I did not give this book the perfect rating because I find that the author should introduce concepts intuitively before giving their mathematical definitions. I am aware that this can be a consequence of a lack of mathematical maturity on my part. If you are tired of reading books on genetic algorithms that talk about natural selection,etc. but do not formalize the concepts involved, then this a book for you.

A Great Starting Point for GA Research.......2001-10-06

I was lucky enough to have Dr. Vose for a graduate course in Genetic Algorithms at the University of Tennessee. The course content was very similar to this book, and gave me the knowledge needed to successfully apply GA's to a wide range of real-life problems. Dr. Vose is a gifted mathematician and computer scientist, and I highly recommend this book.

dense but informative.......2001-01-09

the word simple in the title can be misleading. it is not meant to give the impression that the material in the book is simple, but to say that the topic covered is reduced to the simpliest of genetic algorithm theory, and then, you are brutally raked over burning coals by it.

the introduction given by the author could be mistaken as elitest, totalitarial propaganda for the next mathematical reich: condeming the application and biological euphamism that has been used to explain genetic algorithms while carrying the flag of pure mathematical abstraction. basically, all math and no play makes jack a dull boy, at least to those who wanted a simple introduction.

i found the math sometimes unnecessarily complex at times, with notation being abused (ironically, the author in the introduction condems those that do this, too), and the level of rigor being uneven. all of this makes it sometimes difficult to follow. but, there are some sections that where there is no better explanation in any book but the detailed, well thought out, straight forward presentation here (look at the coverage of walsh and the complex examples sections).

for anybody who uses ga's daily, this is an essential read for a truly deep understanding. the two friends that i have loaned this book to, returned it in under a month scared away by the mathematics, prima facie; it really isn't that bad and the understanding you get from this book is unparalled by any other dna-glossy-picture, darwinian-explanation filled excuse for a book.

One person from Galicia.......2000-05-26

Me ha ayudado mucho en mi trabajo.

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