Genetic-Algorithms

After an introductory review of the literature, Gen and Cheng write a chapter on each class of optimization problems that have been solved with genetic algorithms. Within the chapters they systematically describe each technique that has been used to apply genetic algorithms to the particular problem. Descriptions include the underlying math and a step-wise procedure for implementation. These are backed up with multiple graphs and illustrations, and copious references (737 references in all). A few more actual examples would have helped, especially in the Multiobjective Optimization chapter. The clear organization and number of references make this an excellent reference for the practitioner, but the level of detail could make it difficult for someone new to the technique.

Although the too-cool title made me suspicious, I got this book because I read an excellent review of it (on this page). I was hoping to read a forward-looking thesis on multimedia avatars, but got instead a book which should have been called "Having Fun with Director." It's not about programming, artificial life or avatars. If you have never heard of genetic algorithms or object-oriented programming and think the internet is an incredible source of useful information, then perhaps this book is worth a quick read. Otherwise skip it.

Some people may be put off by the author's referrences to magic and sorcery in his titles. In my opinion, Peter Small has something important to say to those of us interested in knowledge management and Web marketing. An avatar is an animated character on a computer screen and may represent a real person in a virtual world. In my opinion, the importance of avatars is not so much in the programming behind them (as impressive as that may be) but in the human willingness to attribute emotion and intelligence to avatars. An avatar that can access a variety of forms of multimedia, can learn from a variety of sources, and can visually represent emotion is of great potential consequence. Peter Small is a visionary and makes some pretty "radical" statements in his books. This is about the juncture of artificial intelligence, object-oriented programming, and animated interface design. That is potentially a very rich juncture. I wish there was a virtual community of people interested in the practical applications of Mr. Small's ideas.

This book has three subject matters which all run in parallel, informing and commenting on each other.

These subjects are: * the relationship between biological entities and computer objects * the future of the internet * OOPs programming in Director

The book is very clearly and cleverly written. The Lingo scripting, for example, is discussed in the main text in terms of its underlying principles, and the actual scripts are shown in illustrations, reproducing Director's script window. This means that the underlying arguments can be read without interruption, and by readers who have no Lingo experience.

Indeed many of the arguments in the book are addressed to a much wider audience than Director users and Lingo programmers. Peter Small suggests through a series of analogies and practical examples that there may be less difference between human and artificial intelligence than is normally thought - if we concentrate on the effects of intelligence rather than getting caught up in arguments as to what intelligence is and where it comes from.

He uses a wide range of examples, introducing the idea of Hilbert Space as his final conceptual flourish. Against the odds he even manages to explain this abstruse mathematical concept clearly and simply, and then demonstrate convincingly how it can be a useful tool for thinking about the future development of multimedia.

Peter's concern with multimedia lies in the development of 'intelligent' multimedia entities that he refers to as avatars - entities which can grow and change, accessing information on local hard disks, on CD-Roms and on the world wide web. The primary difference between these and traditional bots is that they are designed to operate from a client oriented perspective, rather than the more usual server side emphasis. They are designed to grow organically, to exceed the original intentions of the original programmers. They are designed to be diverse and different, and to use that as a strength.

In many ways Peter is proposing a complete inversion of the way we currently see the Internet. It is usually seen as a new broadcasting medium - I have a website and you can tune into it. Peter suggests that this is a very limited and limiting way to see what is essentially a huge repository of information, all able to be communicated in any way we can imagine. He suggests that the idea of the standard, generalised browser is an idea whose time has more or less gone. Instead he proposes specialised avatar systems who can respond to their users needs and desires and extend themselves across the web to bring back information in useful and structured forms.

One of his demonstrations concerns the construction of a café which can be used to bring like-minded people together, while another concerns avatar web-bots which can be sent off in search of like-minded people to bring to the café. Both of these are described in terms of the fundamental principles, their likely effects - and the Lingo necessary to construct them.

For readers with no Lingo experience Peter provides convincing arguments with just enough technical detail to demonstrate that what he is talking about is not science fiction but can be done today with standard software.

For readers who do have Lingo experience, there is plenty to chew on in the accompanying illustrations of scripts. Here Peter provides the details of how various avatar systems can be built and extended. In addition to the café and web-bots, these include a chemist who is able to work out the correct set of ingredients from sixty million possible combinations in less than 38 steps, taking a second or less in total. Peter uses this as the basis for discussing genetic algorithms, which can be used to model complex thought processes, and which can learn from their experiences, becoming more intelligent the longer they are allowed to 'live'.

Most interestingly of all, though, Peter intends to work out the implications of what he is suggesting in practice on the web. The book is therefore a starting point for an experiment which will be carried out by Peter and anyone who wishes to join him.

The book is, in effect, an invitation to participate in a uniquely exciting experiment - and there aren't many books you can say that about.

The author presents a general introduction into ga's , then moves over to investment strategies and presents solutions.

Unfortunately, he does NOT give background information on the really interesting things like string patterns used, crossover and fitness function and the like. Futhermore, more than one third of the book is filled with endless tables whose content the reader understands after the first table. I guess it makes for a larger book.

conclusion: very disappointing.

After I read this book I read the review from 1998. I was quite surprised that the prior reviewer felt that way. The book provides an introduction to developing the data required for testing, a methodology for developing a study that would be useful in investment practice. This is a quality effort. This book will not provide (or promise) the reader a turnkey system for conquering the market, however there is a framework for future research.

This book was written in 1994, before many of the books dealing with Neural Networks came out, and so the terminology will seem unfamiliar. If you are willing to work through these differences (and it is not too hard) then there is a great deal to learn here.

Bauer predicted (in 1994) that Genetic Algorithms would become widely used. Bauer also predicted that much of the development would be done in secret. I have not come across them in the last few years and at very least I would have expected to see them as signals for sale from system developers. Additionally, there are a series of books like this one that should have appeared since 1994. A search of Amazon using Genetic Algorithm as the subject and sorting by publication date returns 133 titles. I reviewed these titles and did not find any further investment focused titles. I will use this book as a starting point for my research.

My next book will be Melanie Mitchell's "Introduction to GAs".

This man needs to invest in a good editor. Many times I'd read through half a page or so, stop to think about it and then rephrase it into one or two sentences. Blobs of math appear to be thrown in with little justification, and the book isn't improved by them.

But this book is not only unreadable, it's also not useful. It's more an overview of the area than anything else; it doesn't give adequate information about genetic programming or neural networks. It skims many areas in a close to incomprehensible fashion without covering any in what I would consider to be good detail.

Finally, I'm not dim. I have a PhD myself and am used to ploughing through gibberish. But save your money and don't buy this book (Unless you have a wobbly table that needs fixing).

I agree with the previous reviewer: books should be clear and get to the point. Forget about this one. Get Michalewicz and Fogel's "How to solve it" book. It is MUCH better than this one in all levels: it is better written and the content is more authorative and helpful to novices and experts.

This book is supposed to be a textbook. Maybe that's why it sells so well. I guess I am lucky I didn't have to take a class with this thing.

A very good vision of the evolutionary optimisation techniques not only GA. As well there is an excellent chapter on constraints handling. Maybe it is not one of the easiest book on GA but it is definitely the most useful.

I found this book to be a disappointment. It was hard to follow and lacked explainations in some areas. Too much space was spent on random number theory. Don't let the title mislead you; the source code requires C++ with a visual framework (OWL or MFC). I've seen better; skip this one.

The introduction to the basic concepts of GA's was hard to follow, compared to others I've read. In addition, there were several typos and a paragraph that ended in the middle of a key sentence. Also, I would have preferred code for a console app. IMO, the visual stuff just gets in the way of understanding what's going on.

This is an excellent introduction to genetic algorithms. It is best if you know a little bit about software but the book is so well written that even someone who knows nothing about programming will be able to grasp the basic concepts. I had never heard of genetic algorithms before reading this book. When I tried the first "black box" program I was amazed at how quickly the GA found the solution. Seeing evolution in action had a profound impact on me. If some fundamentalist creationists read this book, and saw how natural selection can be used to find creative solutions to difficult problems, it might open their minds to Darwin.

This book deals with a wide variety of intersting and practical topics such as random number generators and finite state machines. I found the section on robotic ants to be the most interesting. It almost makes you wonder if it is possible to create life in a computer (I guess it depends on how you define life).

The only minor complaint I have is that the examples are written for Microsoft Windows which means that the code is cluttered with a lot of GUI garbage. I would have preferred plain old C or C++ or even pseudo code.

One would have thought from the title of the book that it would contain "... Parallel Genetic Algorithms", however it does not. The book covers statistical analyses of algorithms. The reader is meant to find other references that contain algorithms. It is expensive for such a small book. The author talks 'around' the algorithms, without going into the necessary details to write your own parallel genetic algorithms. There are many plots of evolution, so the author must of had access to the algorithms. This book could have been so much more valuable if it contained the algorithms, rather than just talk about them.

Genetic algorithms are easy to parallelize, but they are difficult to control. In a very concise manner, this book presents some theoretical results derived by the author that show how to make parallel genetic algoritms work for many problems and different architectures.

The book has a lot of new theory that is easy to follow and gives recommendations to make parallel genetic algorithms work well in many circumstances. Although the theory makes many simplyfying assumptions, the examples in the book demonstrate that the models are very accurate and the recommendations made in the book seem very reasonable.

The authors do state in the conclusions that some of the GAs discussed throughout the book do not compare well with the state-of-the-art in place and route tools that have been developed during the last two decades (which is to their credit to mention it.) However, the book makes several very serious mistakes and pitfalls both in the design and implementation of the GAs they try, as well as in the choice of tools to compare their implementations against. For example, in partitioning, they choose for their GA the "obvious" object representation. There has been a lot of work on GAs tailored for "grouping problems" such as partitioning (read Falkenauer) that lead to much better results using an encoding based on "groups representations". Even worse, the algorithms they choose to compare against (standard F-M) is unfair, as there are many F-M based partitioners that beat almost any other algorithm that has been proposed by far!. Not to mention that the benchmarks they use are considered today less than "toy" problems!.
Finally, for a book published in 1999, the bibliography offered is missing a lot of important papers published during the 90's in the fields of physical design for VLSI as well as Genetic Algorithms.

This book describes the application of genetic algorithms to electronics design in a clear, consise, and easy to understand way. Starting with a brief introduction covering terminology and concepts, it quickly moves to applications which facilitate comprehension via example usage. The facet I most appreciate about the book is its ability to apply the technology to real world problems while retaining a close connection with theory. While basic utilization is covered, advanced topics are also presented without sacrifice of detail.

This work specific to electrical engineering, in conjunction with Goldbergs's broader treatment of the general subject, together constitute an essential and complete treatment for both the experienced and learning engineer. I have been fortunate to attend professional lectures by one of the authors (Rudnick) and can attest her clarity of expression and ability to easily cover complex material is present throughout the text.

The author's lucid treatment of the subject makes this the fundamental work on application of GA technology to VLSI design.

I bought this book because it has source code for simulated annealing, genetic algorithms, tabu search and neural networks.
I have used the three first source code and ... they are so buggy.
They're wrote in C but doesn't compile due to evident syntax errors (so evident, are they here so as to made these source code unusable ?).
The content of the book is not equilibrate (some metaheuristics aren't discussed thoroughly).
If the authors use there source code, I think evereything presented in this book is completely false.
Don't buy it, there are some better books to buy.

This book gave a superficial coverage on 4 optimization techniques in Chapter 1. Out of the 4 techniques, only Genetic Algorithm was explained slighty more in detail. The rest are merely short examples. That is about all you will get from this book (one brief Chapter on ALL 4 techniques).

This is a very useful book. It starts by introducing very important and interesting techniques in optimisation i.e. genetic algorithms, simulated annealing, tabu search and neural networks. It continues by giving examples of how the techniques have been applied in various case studies. The book also contains code so that users have a head start in implementing the techniques described. This book is good for beginners because it describes the basics of the techniques. It is also suitable for more advanced researchers because the case studies provoke ideas for further work. In conclusion, this book is a useful addition to the bookshelf of any researcher interested in intelligent optimisation.

The information was useful & approprate to the topic. I'd rank it as an average quality refence but a very poor text book.

The text is poorly written. The code is simple and easy to understand, but not very object oriented. There is not enough explanation of the code. The code is not electronically available.

The treatment was very mathematical but lacking in explanation & application examples. There were plenty of deffinitions, but not enough examples.

The topics covered in this book are all important from the standpoint of applications in physics, engineering, computer science, financial engineering, and computational biology. It is written for the person just getting started in these topics, and the author does a fairly good job of discussing them. Readers should not expect, and they will not get, in-depth discussions on these topics, as this would swell the book to 10 times the size. They will however get preparation for moving on to more advanced and complete treatments.

Nonlinear and chaotic maps are considered in chapter 1, with elementary definitions given and six different examples of maps discussed. In discussing the calculation of numerical trajectories of maps, the author deals with the problem of large initial values for the maps and how to implement these in SymbolicC++ and Java. He also shows how to write/read data to a file using C, C++, and JAVA. The exception handling capability of JAVA comes out nicely, but no performance comparison between the three languages for simulating the maps is given by the author. The language REDUCE is used to discuss the stability of the fixed points of the logistic equation, but the code would be useless to the reader who did not have REDUCE since some of the function calls are hidden from the reader. Useful programs are given for calculating the Lyapunov and autocorrelation functions. In addition, C++ programs are given for evaluating the correlation integral for the Henon map. The programs he develops in this chapter can serve as a quick benchmark for one's own programs that calculate the same quantities.

In chapter 2, the author discusses methods for studying time series, including the Lyapunov and Hurst exponents. These two quantities are of enormous importance in the study of dynamical systems, financial data, and network performance. The C++ program that the author gives for calculating the Hurst exponent will not work for arbitrary time intervals. This is followed in the next chapter by a consideration of autonomous systems of ordinary differential equations. The classification of fixed points is considered, and the important concept of a homoclinic orbit. The author gives a nice JAVA program that finds the homoclinic orbit of an anharmonic differential equation using the Lie series technique. The phase portrait of the Van der Pol oscillator is calculated using the Runge-Kutta technique in a C++ program, along with the Lotka-Volterra system from mathematical biology.

Hamiltonian mechanics is discussed in chapter 4, with the important Henon-Heiles model from astrophysics is discussed and JAVA programs given for studying its behavior using the Poincare section technique. Newcomers to this technique will appreciate seeing it done here explicitly. Integrability of Hamiltonian systems using the Lax representation and Floquet theory are also treated, but only at a very rudimentary level. Dissipation is included in the next chapter, and the author discusses the classification of fixed points according to their stability. Lyapunov exponents are again brought into the picture, and the phenomenon of hyperchaos is discussed. Some bifurcation theory is introduced with an example of the Hopf bifurcation. Chapter 6 studies nonlinear driven systems, with the Duffing oscillator treated, and the author gives a useful program for calculating the autocorrelation function of this system. The controlling of chaos with feedback and non-feedback controls is the subject of the next chapter, mostly in the context of difference maps. Fractals finally get introduced in chapter 8, with iterated function systems defined but proofs of their properties omitted. The author gives programs for calculating various popular fractals, such as the dragon, Sierpinski gasket, Koch curve, the Mandelbrot set, and the Julia set. The main disappointment in this chapter is that the author does not give programs for calculating the Hausdorff dimension or capacity, quantities that are notoriously difficult to get a meaningful computational handle on.

The author switches gears in the next chapter and discusses cellular automata, which have recently made a comeback, especially in research on quantum computation. The discussion is too brief however, and does not allow the reader to gain an appreciation of the properties of these important objects. Chapter 10 gives a brief overview of some techniques for solving differential equations, such as the Euler method and the Lie series technique. The latter is not commonly treated in beginning books so its inclusion here is helpful. Symplectic integration is also discussed briefly, but the author does not discuss how to check the integrators using backward integration, which is commonly used in conservative systems modeled by symplectic maps.

Chapter 11, covering neural networks, is the most well-written in the book, and the newcomer to the field will get a fairly decent introduction to the subject. The supplied programs serve to illustrate some of the important concepts in neural networks, such as the Hopfield model, the Kohonen network, the perceptron learning algorithm, and the back-propagation algorithm.

Chapter 12 is an introduction to genetic algorithms, and I find this one particularly nice also, as it does give a rudimentary introduction to what evolutionary algorithms are all about, and gives some elementary genetic programs that find the maximum of one- and two-dimensional maps. He also discusses simulated annealing, and gives a useful program that allows the reader to see clearly how this technique works.

The last chapter covers fuzzy sets and fuzzy logic, which has also taken on importance in recent years, especially in data mining and financial engineering. The programs given to illustrate the concepts are particularly interesting from the standpoint of coding in C++, as the author uses friend functions and operating overloading in some of them. The reader gets a good overview of fuzzy reasoning and fuzzy rule-based systems.

I only browsed the book but I perfectly agree with my friend from milano. Another one of those little useless trash marked with high price because they knew there would be few buyers. How can my favorite publisher Springer join this business? (John-Wiley seems to be the pioneer on this.)

Java being the choice language for enterprise no-bs concrete applications I was expecting a practical viewpoint and a hands-on approach in this book. It turns out this skimpy booklet is
more of a an extended paper, something like a thesis with no practical value ( and hence no value at all). It won't teach you neither GA algorithms nor how to use Java to code them. The Java
word was put in to fool buyers exploiting the Java marketing wave. The only java GA algorithm presented in this book is in appendix B.... can it get worse than this?

i'm not sure this book is a good idea to start with evolutionary algorithms. The treatment it provides is certainly of an acceptable level, but it is too short to provide a deep understanding of how genetic algorithms work. If you want to understand how GAs work, Goldberg is THE place where to start. The code there is in Pascal, but frankly i don't see the advantage of Java for coding evolutionary algorithms for the first time. Maybe only after one has mastered the internals of genetic algorithms one can go to Java for advanced evolutionary algorithms for more complex tasks. One has first to understand well how data structures and operators work with a rather low-level language (C or C++), and then use Java for higher-level algorithms.

So although the content of this book is not bad at all, i'm not sure it's well-suited for newcomers, and for advanced people the material is too simple for bringing something new. The only contribution of this book from my viewpoint is the large number of websites where evolutionary code is available.