Genetic-Algorithms

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Book reviews for "Genetic-Algorithms" sorted by average review score:
Foundations of Genetic Programming
Published in Hardcover by Springer Verlag (March, 2002)
Authors: Riccardo Poli and William B. Langdon
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The modern revolution
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
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
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.

GECCO'99: Proceedings of the Genetic and Evolutional Computation Conference Set
Published in Paperback by Morgan Kaufmann (15 October, 1999)
Authors: Wolfgang Banzhaf, Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, Robert E. Smith, and Gecco
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Two volumes!
The proceedings really have 1876 pages, but they are divided into two volumes. The first volume has 944 pages, the remaining pages are in the second volume. Make sure you get the two volumes!

magnificent and fascinating
"The 1999 Genetic and Evolutionary Computation Conference (GECCO-99) combines the longest running conference in evolutionary computation (ICGA) and the world's two largest EC conferences (GP and ICGA) to create a unique opportunity to bring together the best in research in the growing field of genetic and evolutionary computation (GEC).

"The GECCO conference continues the tradition of the GP and ICGA conferences of bringing together researchers from the entire spectrum of research in evolutionary computation, including genetic algorithms, classifier systems, genetic programming, evolvable hardware, DNA and molecular computing, evolutionary strategies, evolutionary programming, artificial life, adaptive behavior, agents, as well as real-world applications of all of these areas." - from the publisher.

The relentless high quality of the many papers in this book make it delightful and thought-provoking. (My copy is 944 pages - not "1876" as Amozon has it.)

Genetic Algorithms and Engineering Design
Published in Hardcover by Interscience (07 January, 1997)
Authors: Mitsuo Gen and Runwei Cheng
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a very nice book for beginner
This is a very nice book for beginners. It is a well-organized and excellent text covering fully up-to-date treatment of genetic algorithms in industrial engineering and operationas research. Especially, its unique, creative and intuitive approach makes the complex subject transparent and understandable. I fond the book very much and used the materials in my classes.

a nice book for a beginner
It is a very nice book for a beginner. It is a well-organized and excellent text covering fully up-to-date treatment of genetic algorithms in industrial engineering and operationas research. Especially, its unique, creative and intuitive approach makes the complex subject transparent and understandable. I fond the book very much and used the materials in my classes.

Genetic Fuzzy Systems
Published in Hardcover by World Scientific Pub Co (July, 2001)
Authors: Oscar Cordon, Francisco Herrera, Frank Hoffmann, and Luis Magdalena
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A major source on genetic fuzzy systems
The volume brings an outstanding presentation of the major issues, ideas, concepts and algorithms to design and develop fuzzy systems using gentic algorithms. A field of major relevance for researchers and practioners, genetic fuzzy systems provides a major methodological substract of significant impact in practice. The book is unique in its contents and presentation. Chapters begin with the key concepts and smoothly grows to advanced concepts in a clear and very understandable and motivating way. The material mirrors the state of the art in the area of genetic fuzzy systems and contains the most recent results available until its publication. Written by renowned, internationally recognized researchers, the book is mandatory to all who are interested in the field of computational intelligence, its foundations and applications.

Summary of contents by the author
In recent years, a great number of publications have explored the use of genetic algorithms as a tool for designing fuzzy
systems. Genetic Fuzzy Systems explores and discusses this symbiosis of evolutionary computation and fuzzy logic. The book summarizes and analyzes the novel field of genetic fuzzy systems, paying special attention to genetic algorithms that adapt and learn
the knowledge base of a fuzzy-rule-based system. It introduces the general concepts, foundations and design principles of genetic fuzzy
systems and covers the topic of genetic tuning of fuzzy systems. It also introduces the three fundamental approaches to genetic learning
processes in fuzzy systems: the Michigan, Pittsburgh and Iterative-learning methods. Finally, it explores hybrid genetic fuzzy systems such as
genetic fuzzy clustering or genetic neuro-fuzzy systems and describes a number of applications from different areas. Genetic Fuzzy System represents a comprehensive treatise on the design of the fuzzy-rule-based systems using genetic algorithms, both from
a theoretical and a practical perspective. It is a valuable compendium for scientists and engineers concerned with research and applications in
the domain of fuzzy systems and genetic algorithms.

Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! (Kluwer International Series in Engineering and Computer Science, 438)
Published in Hardcover by Kluwer Academic Publishers (June, 1998)
Authors: William B. Langdon and Koza John R
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useful and practical description of Genetic Programming.
Langdon's book is an important addition to the literature on Genetic Programming. After a thorough survey of the state of the art in the field, he describes how Genetic Programming can be used to generate the standard data structures that humans rely on so heavily. He then describes how these data structures can be used to extend the power of Genetic Programming, and gives a number of useful examples. In all, an important part of any GP library.

A very nice introduction to the field of genetic programming
The book "Genetic Programming + Data Structures = Automatic Programming!" by William B. Langdon is a very nice introduction to the field of genetic programming. The book is well structured. It contains most, if not all, references to important previous work on GP, and further extends the range of applicability of GP by investigating the use abstract data structures in evolving genetic programs. Routines to manage abstract data structures (stacks, queues, and list) are evolved by GP. The book also shows some problems where enriching the GP-language with primitives to manage a data structure enables it to solve more complex problems.

Cees H.M. van Kemenade.

Advances in Fuzzy Logic, Neural Networks, and Genetic Algorithms: Ieee/Nagoya-University World Wisepersons Workshop Nagoya, Japan, August 9-10, 1994: Selected Papers (Lecture Notes in Computer Science, Vol 1011)
Published in Paperback by Springer Verlag (November, 1995)
Author: Takeshi Furuhashi
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fuzzy seloution
i like solve any problem with fuzzy thought. i like this method. i like every one think about this subject.

Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Published in Hardcover by Kluwer Academic Publishers (01 October, 2001)
Authors: Pedro Larranaga, Jose A. Lozano, and Pedro Larraanaga
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Many topics covered, some chapters are a little weak
This is a good book to learn about Estimation of Distribution Algorithms (EDAs or also called DEAs or Iterated DEAs). These algorithms are similar to evolutionary algorithms, but do not use the crossover or mutation operators of evolutionary search. EDAs instead create a probabilistic model of good solutions and use the model to generate new search points. It's a nifty idea and it works.

Most of the chapters of this edited collection were authored or coauthored by the editors. So, algorithms developed by other people do not get a lot of attention. However, the editors (or is it the authors) manage to include chapters on combinatorial, continuous, and discrete optimization.

There is a section on machine learning applications that is OK, but the last chapter on training neural nets with EDAs is very weak (look ma I used this and it worked...). Except for this chapter, the rest of the chapters in this section use careful experiments and statistics to make their points.

Making the source code available would have improved things and would make it easier for people to try these algorithms.

Evolutionary Algorithms for Single and Multicriteria Design Optimization
Published in Hardcover by Physica Verlag (November, 2001)
Author: Andrzej Osyczka
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Excellent book
It is very good book for people who works in structural optimization. It has many nice examples. I will buy it for my library.

Evolutionary Algorithms in Engineering and Computer Science : Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming and Industrial Applications
Published in Hardcover by John Wiley & Sons (22 October, 1999)
Authors: K. Miettinen, Pekka Neittaanmäki, M. M. Mäkelä, and Jacques Périaux
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Excellent review of the status of GA's
This book is perfect to bring the practioner or researcher up to date with the state of the art in the field of GAs. Because each chapter is authored by different contributors, the style varies a lot. However, the book succeeds in its unifying theme. Probably the most useful thing accomplished here is to introduce the research community to the reader so that one can pick up reading directly from the fast evolving literature. Enjoy.

Genetic Programming : An Introduction : On the Automatic Evolution of Computer Programs and Its Applications
Published in Hardcover by Morgan Kaufmann (01 December, 1997)
Authors: Wolfgang Banzhaf, Peter Nordin, Robert E. Keller, and Frank D. Francone
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Imagine a world in which computers program other computers based on strategies borrowed from biology and natural selection. Genetic Programming: An Introduction explores fascinating possibilities like these in a thriving area of computer-science research. This research-quality book is for anyone who wants to see what genetic programming is and what it can offer the future of computing.

This text begins by situating genetic programming in terms of the history of computing and machine learning. Early sections show the links between Darwinism, molecular biology, and genetic programming. (Genetic programming uses the strategy of natural selection by solving a problem in successive iterations, which produces the "fittest" solution, much like new species evolve in the natural world.)

The authors present a lot of molecular-biology background since it is central to the genetic-programming project. (There are interesting parallels here. Just as our DNA contains inert information, programs developed using genetic algorithms usually contain many "extra" instructions, too--which often leads to bloated, though effective, code in the final product.) Even though this is extremely technical material, the authors do manage to engage the reader in the imaginative leap from Darwin and DNA to computers and the world of genetic programming.

Later chapters define what genetic programming is and what strategies it uses to let computers program themselves. The authors also examine the state of the art of genetic programming and define what problems need to be solved before it can be widely adopted. The amount of research in this section will mostly benefit specialists in the genetic-programming field.

A later chapter on applications that use genetic programming offers dozens of papers, with applications of this approach from a wide variety of fields, including biology, industry, and computers (and some impressive technologies such as robotics and data mining). Though the authors exaggerate somewhat on how "real world" these applications are, it's clear that genetic programming will continue to improve and find its way into more areas of computing--with even more productive results. Though coding by humans is safe for the foreseeable future, genetic programming offers an appealing alternative to some kinds of problems. --Richard V. Dragan

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Good as an overall, not for the details
This book is good for getting a general view of genetic programming. Nevertheless, I think it neglects many details. For example, it is very hard to from the book how a simple selection strategy (tournament selection) works in practice.

I do not think this book is useful for someone intending to code a genetic programming algorithm.

terrific textbook
I skimmed the Koza books (GP: I & II) and this one at the store. Using the layout, chapter names, and the introductory chapters as my guide, I decided to buy this book to introduce me to the current state of the art in GP. The strengths of this book are its textbook format and the informal exercises that are presented for the reader at the end of every chapter. There is also a great deal of compilation from other relevant gp works presented in a localized, intra-chapter basis. The book is thus highly digestable to a newcomer, and is a far less time-consuming way to learn about GP than through the "expert" papers on the web. Having now almost finished the book, I feel that I am ready and able to author and apply GP techniques in a wide variety of applications and languages, having spent less than 20 hours in study time. A terrific achievement by Banzhaf and company, highly recommended.

Excellent, comprehensive and easy to read.
We all know that kind of books where the author likes to show how much he knows making things intentionally complex....well...this is the opposite side of the spectrum.
The book is very complete and detailed yet easy to read, even after a day of work.
The first part of the book contains introductory information on background areas like probability, biology and computer science as a general discipline.
Getting into the topic, it clarifies some of the differences between evolutionary systems and genetic algorithms and shows how all this contributes to the theory of genetic programming and the evolution of computer programs.
It explains how things are done with different types of individuals (tree, linear, graph, etc) and gives valuable insight about the implementation process.
Although you may need other sources for formal treatment of some topics, this book is a very good acquisition.

Related Subjects: General-Average
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