An Introduction to Genetic Algorithms (Complex Adaptive Systems)

Book Description

Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics--particularly in machine learning, scientific modeling, and artificial life--and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics.

Customer Reviews:

Good Theoretical GA Textbook.......2005-05-06

This book primarily deals with the theoretical side of genetic algorithms. If you are looking for practical knowledge of how to implement a GA you should look elsewhere. For all intents and purposes this is a textbook. It's heavy on theory and proofs, but doesn't always explain everything in depth (that's what class time is for). There are problems at the end of each chapter that can be assigned to students.

There are case studies of many academic projects that seem to drone on forever and aren't really that useful in helping you learn how to write your own GA. Chapter 1 gives an overview and provides all of the appropriate terminology. Chapter 5 gives an high-level overview of how to implement a GA. Those are the 2 must-read chapters, all of the others can be used as torture for CS students.

To recap, if you're teaching a class in artificial intelligence this book is good. If you're trying to figure out how to implement a GA to solve a practical problem not so good. That evens out to 3 stars for my rating. I recommend searching the web, there are a few good sites on GA programming.

Not for beginners.......2004-02-04

I have an engineering degree, and I found this to be a little tough to follow for two reasons:

1. Not enough step by step prodecure especially at the beginning. Mitchell is too quick to start with the math formulas. It turns out that Genetic Algorithms are fairly straight forward and easy to follow, but you have to read this book twice before you "get it" because Mitchell clouds the discussion with proofs and mathematical representations of systems. It is tough to follow.

2. Mitchell does a poor job of selecting meaningful examples to illustrate the points. A nice simple set of examples where the average person easily picture the system would have been delightful. Instead this author chooses to illustrate the Genetic Algorithms through uncommon neural networks amoung other exotic applications. I found myself struggling to understand both the example (I didn't know a thing about neural networks!) and the genetic algorithm.

When buying an Introduction type book, I expected it to be more 'down to earth'. this book is for advanced minds!

An introduction and much more.......2004-01-26

First it must be said that the book is not an introduction that the non-scientist will easily understand. Some knowledge of computer programming is assumed. It acknowledges this in the last paragraph of the preface. Many of the notations in the book are unfamiliar to business or financial readers. There is no mathematics beyond algebra so the aforementioned prerequisites are the main hills to climb.

Mitchell's book is an overview of genetic algorithm analysis techniques as of 1996. The author gives a history of pre-computer evolutionary strategies and a summary of John Holland's pioneering work. A description of the basic terminology is presented and examples of problems solved using a GA (such as the prisoner's dilemma). The second chapter discusses evolving programs in Lisp and cellular automata. Also included in this chapter is a discussion of predicting dynamical systems. This was the section that has the most interest for me. Also interesting was the summary in this chapter about putting GAs into a neural network so that the ANNs could evolve.

The fifth chapter discusses when to employ a GA for maximum success. I appreciate the clearly thought out discussion of when to choose a GA for a problem. Sometimes authors of these types of books mimic the man with a hammer that thinks everything looks like a nail.

A Great Introduction to Genetic Algorithms.......2002-12-07

This is a great place to start to learn about genetic algorithms. The writing is clear and not bogged down by jargon. The book is not overly technical; it is written for the layman and has a casual conversational style that is a pleasure to read.

About half of the book is devoted to presenting examples of studies that have used genetic algorithms. These examples are interesting in themselves and also serve to illustrate the variety of genetic approaches that are available. The book also presents conflicting points of view of experts about which algorithms work best and why. This is helpful in combatting the impression that a beginner sometimes gets that everything is simple and all the answers are known.

Good introduction for such a short book.......2002-04-07

Although short, this book gives a good introduction to genetic algorithms for those who are first entering the field and are looking for insight into the underlying mechanisms behind them. It was first published in 1995, and considerable work has been done in genetic algorithms since then, but it could still serve as an adequate introduction. Emphasizing the scientific and machine learning applications of genetic algorithms instead of applications to optimization and engineering, the book could serve well in an actual course on adaptive algorithms. The author includes excellent problem sets at the end of each chapter, these being divided up into "thought exercises" and "computer exercises", and in the latter she includes some challenge problems for the ambitious reader.

Chapter 1 is an overview of the main properties of genetic algorithms, along with a brief discussion of their history. The role of fitness landscapes and fitness functions is clearly outlined, and the author defines genetic algorithms as methods for searching fitness landscapes for highly fit strings. An elementary example of a genetic algorithm is given, and the author compares genetic algorithms with more traditional search methods. The author emphasizes the unique features of genetic algorithms that distinguish them from other search algorithms, namely the roles of parallel population-based search with stochastic selection of individuals, and crossover and mutation. A list of applications is given, and two explicit examples of applications are given that deal with the Prisoner's Dilemna and sorting networks. The author also gives a brief discussion as to how genetic algorithms work from a more mathematical standpoint, emphasizing the role of Holland schemas. The reader more prepared in mathematics can consult the references for more in-depth discussion.

The next chapter stresses the role of genetic algorithms in problem solving, beginning with a discussion of genetic programming. Automatic programming has long been a goal of computer scientists, and the author discusses the role of genetic programming in this area, particularly the work of John Koza on evolving LISP programs. In addition, she discusses the current work on evolving cellular automata and its role in automatic programming. The latter discussion is more detailed, this resulting from the author's personal involvement in artificial life research. Those interested in time series prediction tools will appreciate the discussion on the use of genetic algorithms to predict the behavior of dynamical systems, with an example given on predicting the behavior of the (chaotic) Mackey-Glass dynamical system. The author also gives applications of genetic algorithms in predicting protein structure, an area of application that has exploded in recent years, due to the importance of the proteome projects. The area of neural networks has also been influenced by genetic algorithms, and the author discusses how they have replaced the familiar back-propagation algorithm as a method to find the optimal weights.

Chapter 3 is more in line with what the author intended in the book, namely a discussion of the relevance of genetic algorithms to study the mechanisms behind natural selection. She discusses the "Baldwin effect", which gives a connection between what an organism has learned (a small time-scale process) to the evolutionary history of the Earth (a long time-scale process). A simple model of the Baldwin effect is given using a genetic algorithm, along with a discussion of the Ackley-Littman evolutionary reinforcement learning model, which involves the use of neural networks, and which is another computational demonstration of the Baldwin effect. In addition, the author discusses models for sexual selection and ecosystems based on genetic algorithms. These are the "artificial life" models that the author has been involved in, and she gives a very understandable overview of their properties.

Chapter 4 should suit the curiosity of the mathematician or computer scientist who wants to understand the theoretical justification behind the use of genetic algorithms. Again employing the Holland notion of schemas and adaptation as a "tension between exploration and exploitation", the author formulates a mathematical model, called the Two-Armed Bandit Problem, of how genetic algorithms are used to study the tradeoffs in this tension. The level of mathematics used here is very elementary with the emphasis placed on the intuition behind this model, with only a sketch of the model's solution given. To address the role of crossover in genetic algorithms, the author discusses in detail a class of fitness landscapes, called "Royal Road functions" that she and others have developed. The performance of the genetic algorithm employed is then compared against the three different hill-climbing methods. Formal mathematical models of genetic algorithms are also discussed, one of which involves dynamical systems, another using Markov chains, and one using the tools of statistical mechanics. The latter is very interesting from a physics standpoint but is only briefly sketched. The interested physicist reader can consult the references given by the author for further details.

Practical use of genetic algorithms demands an understanding of how to implement them, and the author does so in the last chapter of the book. She outlines some ideas on just when genetic algorithms should be used, and this is useful since a newcomer to the field may be tempted to view a genetic algorithm as merely a fancy Monte Carlo simulation. The most difficult part of using a genetic algorithm is how to encode the population, and the author discusses various ways to do this. She also details various "exotic" approaches to improving the performance of genetic algorithms, such as the "messy" genetic algorithms. One must also choose a selection method when employing genetic algorithms, and the author shows how to do this using various techniques, such as roulette wheel and stochastic universal sampling. In addition, genetic operators must also be chosen in implementing genetic algorithms, and the author emphasizes crossover and mutation for this purpose. Lastly, the values of the parameters of the genetic algorithm, such as population size, crossover rate, and mutation rate must be chosen. The author discusses various approaches to this. Although brief, she does give a large set of references for further reading.

Robots, Androids and Animatrons, Second Edition : 12 Incredible Projects You Can Build
Average customer rating:

Very good Hobbiest book

Very good book!!

I'm glad I finally found a decent book on pics!

Projects can be a bit pricy.

This book has changed my life.

Robots, Androids and Animatrons, Second Edition : 12 Incredible Projects You Can Build
John Iovine
Manufacturer: McGraw-Hill/TAB Electronics
ProductGroup: Book
Binding: Paperback

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Robot Building for Beginners

ASIN: 0071376836

Book Description

Bring a robot to life without programming or assembly language skills!

There’s never been a better time to explore the world of the nearly human. With the complete directions supplied by popular electronics author John Iovine, you can:

• Build your first walking, talking, sensing, thinking robot
• Create 12 working robotic projects, using the fully illustrated instructions provided
• Get the best available introduction to robotics, motion control, sensors, and neural intelligence
• Put together basic modules to build sophisticated ‘bots of your own design
• Construct a robotic arm that responds to your spoken commands
• Build a realistic, functional robotic hand
• Apply sensors to detect bumps, walls, inclines , and roads
• Give your robot expertise and neural intelligence

You geteverything you need to create 12 exciting robotic projects using off-the-shelf products and workshop-built devices, including a complete parts list. Also ideal for anyone interested in electronic and motion control, this cult classic gives you the building blocks you need to go practically anywhere in robotics.

Customer Reviews:

Very good Hobbiest book.......2006-11-10

Plenty of information for make very good projects, unfortunately for my taste, the autor only works with PIC microcontrollers, perhaps a very good book

Very good book!!.......2003-09-18

This book is great for the beginner. Iovine explains the subject matter in a way that makes it exciting and fun. He has a way of getting the exciting vision of building robots across to the reader. The book makes it easy to get parts needed for the projects from the authors web site. I'm really looking forward to his new book, Pic Robotics!!!

I'm glad I finally found a decent book on pics!.......2002-06-03

This was the 3rd book I purchased on pics. The other 2 books - one by Myke Predko (awful), the other by David L. Benson (dissapointing.)

I wish this had been the first. Although not geared specificly towards pics, that was my reason for buying it. I was interested in pics and robotics; so this book was right up my alley.

Admittedly the book has numerous plugs for a company the guy obviously works for, owns, or gets kickbacks from! And he wants you to put out a considerable about of cash from the get go to purchase items he wants you to use in order to follow along with him. However, that doesn't bother me. I never build any projects I see in these type of books. I only use them for learning - I build my own projects.

This book did teach me quite a bit about pics. Which was my goal. He didn't bog you down with the history or innards of pics like other books. Which I am not interested in. The book was a great mixture of hardware and software topics...

I would recommend this book to anyone interested in pics... Subsequently I purchased another book by him simply because I saw his name on it and I wasn't dissapointed! I'm looking forward to other books by John Iovine in the future...

Projects can be a bit pricy........2002-03-13

The illustrations and text I found to be very helpful for a project I was working on, but the supplies the book recommends can usually de difficult to find, and can tend to be a bit pricy.
I recommend visiting a local toy store after deciding on a project, and buying toys with the parts you need. Its more fun to make one thing into another anyway.

This book has changed my life........2002-01-23

I bought this book with the humble desire of creating a simple companion. After I finished, the Creation turned on me and my family and reprogrammed my VCR. It then proceeded to change the message on my answering machine. It somehow convinced my Nissan truck not to allow me inside. I fear for my life. How could I have let it go this far? All I really wanted was a cute little Furby but I ended up with a cyber-monster with dreams of wiping out humans and creating a robotic Utopian Hell.....other than that, the book was pretty cool.

Life: An Introduction to Complex Systems Biology (Understanding Complex Systems)

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Life: An Introduction to Complex Systems Biology (Understanding Complex Systems)
Kunihiko Kaneko
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 3540326669

Book Description

What is life? Has molecular biology given us a satisfactory answer to this question? And if not, why, and how to carry on from there? This book examines life not from the reductionist point of view, but rather asks the question: what are the universal properties of living systems and how can one construct from there a phenomenological theory of life that leads naturally to complex processes such as reproductive cellular systems, evolution and differentiation? The presentation has been deliberately kept fairly non-technical so as to address a broad spectrum of students and researchers from the natural sciences and informatics.

Average customer rating:

Great Content, Author Can't Explain Clearly Though
At times cryptic, but nevertheless marvellous
Hard Science
An excellent textbook for this rapidly changing field.

Introduction to Artificial Life
Christoph Adami
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 0387946462

Book Description

Life is so diverse and complex that is seems impossible to extract the general principles governing each individual living system. Fortunately, however, the unrelenting growth of the power of modern computers has opened up entirely unexpected avenues of opportunity for us in exploring the construction of artificial living systems. This has created the possibility to design and conduct dedicated experiments with these systems, and has generated interest in the idea of formulating a set of "general principles of the living state" which are quite independent of a particular implementation. Such a "theory of living systems" might equally well-predict the outcome of experiments performed on the protean living system which gave rise to life on earth, e.g., and RNA world, and those worlds in which information is coded in binary strings compiled to programs that have the ability to self-replicate: thus and instance of "Artificial Life." This book and CD-ROM have been developed in a lab-oriented course taught at Cal Tech in 1995 and 1996, and simultaneously augmented by Artificial Life research conducted there. The courses have been attended by an interdisciplinary group of students from backgrounds in physics, computer science, and the computational neural sciences. Pre- requisite understanding of statistical physics and thermodynamics, basic biology, as well as familiarity with computer architectures and scientific computing techniques are assumed. This project is an attempt to bring together the necessary theoretical groundwork for understanding the dynamics of systems of self-replicating information, as well as the result from initial experiments carried out with artificial living systems based on this paradigm.

Customer Reviews:

Great Content, Author Can't Explain Clearly Though.......2000-11-14

I bought this book to understand the mathematics and physics in A-Life and Complexity. Instead I found this book very long winded and difficult to comprehend exactly what was trying to be said. The content and layout of the book is great, just wish a better writer had been the author of this book. Lots of fancy, big words that are not needed to get the basic points across. Very hard to understand what is being said. It takes smarts and skill to explain complicated, abstract ideas in a meaningful manner. This book does not do that. I wish it did!

At times cryptic, but nevertheless marvellous.......2000-06-02

This is the ONLY book I have seen which brings together all the many and various strands which are essential to the exciting new subjects arising currently around the question: What is Life? It is a stunning tour de force of the basic knowledge you need to possess to work in the areas of A-life or biological complexity.

I should warn: it's not a book I could read through in an afternoon, by any means. At times the descriptions are a little cryptic, so that I had to work at understanding what was being said. But the effort I had to put in was always rewarded with greater understanding. Thank you, Chris Adami.

Hard Science.......2000-05-10

Adami demonstrates how to use the tools of artificial life to conduct pure scientific research. A very clear and readable textbook on the subject, Adami makes me want to go back to graduate school. Here is a chance to take an introductory course in an exciting field of research that is truely table-top science. I loved the book and I didn't even use the CD and software that came with it.

An excellent textbook for this rapidly changing field........1998-08-24

Adami's book is the first comprehensive review of issues pertinent to the field of artificial life. The book is a textbook based on his lectures at CalTech. Some of the topics are a bit brief (Turing machines are summarized in four pages) but that is to be expected for a book whose goal is to integrate concepts from the fields of biology, chemistry, statistics, computer science, information science, etc. I found the book fascinating and Chris includes a CD-rom and several chapters on the Avida simulation developed at CalTech. There are numerous references and problems at the end of each chapter.

Computing with Cells and Atoms: An Introduction to Quantum, DNA and Membrane Computing

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Computing with Cells and Atoms: An Introduction to Quantum, DNA and Membrane Computing
Cris Calude , and Gheorghe Paun
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Brain Dynamics: An Introduction to Models and Simulations (Springer Series in Synergetics)

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Brain Dynamics: An Introduction to Models and Simulations (Springer Series in Synergetics)
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Brain Dynamics is an introduction for graduate students and nonspecialists from various backgrounds to the field of mathematical and computational neurosciences. The approach in this book is through pulsed-coupled neural networks, with at their core the lighthouse and integrate-and-fire models, which allow for the highly flexible modelling of realistic synaptic activity, synchronization and spatio-temporal pattern formation. As a more advanced topic, pulse-averaged equations and their application to movement coordination are discussed. The book closes with a short analysis of models versus the real neurophysiological system. The second edition has been thoroughly updated and augmented by an extensive chapter that discusses the interplay between pattern recognition and synchronization. Further, to enhance the usefulness as textbook and for self-study, the detailed solutions of all XX exercises throughout the text have been added.

Computational Discovery of Scientific Knowledge: Introduction, Techniques, and Applications in Environmental and Life Sciences (Lecture Notes in Computer Science)

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Computational Discovery of Scientific Knowledge: Introduction, Techniques, and Applications in Environmental and Life Sciences (Lecture Notes in Computer Science)

Manufacturer: Springer
ProductGroup: Book
Binding: Paperback

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Book Description

Advances in technology have enabled the collection of data from scientific observations, simulations, and experiments at an ever-increasing pace. For the scientist and engineer to benefit from these enhanced data collecting capabilities, it is becoming clear that semi-automated data analysis techniques must be applied to find the useful information in the data. Computational scientific discovery methods can be used to this end: they focus on applying computational methods to automate scientific activities, such as finding laws from observational data. In contrast to mining scientific data, which focuses on building highly predictive models, computational scientific discovery puts a strong emphasis on discovering knowledge represented in formalisms used by scientists and engineers, such as numeric equations and reaction pathways.

This state-of-the-art survey provides an introduction to computational approaches to the discovery of scientific knowledge and gives an overview of recent advances in this area, including techniques and applications in environmental and life sciences. The 15 articles presented are partly inspired by the contributions of the International Symposium on Computational Discovery of Communicable Knowledge, held in Stanford, CA, USA in March 2001. More representative coverage of recent research in computational scientific discovery is achieved by a significant number of additional invited contributions.

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