Genetic-Algorithms

A good book if you are looking for some applications of GAs and different points of view on how implementing a GA. Some examples include: a Parallel GA, a timetable solution, a finite state machine implementation and much more. But it doesn't goes deep in implementation details. Not recommended for real beginners

the relationship of the stucture of genetic algorithm and its convergencrs

Presents non-standard techniques without pointing out the standard ones. The non-standard techniques were recommended strongly based only on author's personal opinions, without comparison to other standard techniques on broad spectrum.

For starters, it is much better to look into "An Introduction to Genetic Algorithms" by Melanie Michell.

This is a very simple introductory book to read on genetic algorithm. It provides a good overview of the main mechanisms involved. There is no theoretical treatment of the subject. The Pesudcodes provided in the appendixes have some mistakes. I recommend it for people who want quick general outline of what genetic algorithms means and how to apply it.

In my opinion to well understand a process/method you have to follow an example in every little detail. This book does exactly this and once read allows to write your own code easily. I highly recommend this book!

Why this book, when there are several shorter books on GP, and its principle author, John Koza, has written two other, more general and equally voluminous books on GP? This book addresses how to evolve program architecture, that's why! Living organisms didn't grow arms and eyes through simple mutation. It required more subtle genetic operators. Traditional genetic operators (as used in genetic algorithms) may be sufficient for evolving solutions to optimization problems where the structure if not the specifics of each solution is pretty much the same. But to effectively evolve program structures, you need architecture altering genetic operators. This book provides motivations in computer science, foundations in biology, and explanations in English.

Genetic programming is like a new Big Bang in computer universe.
Reach the automatic programming level is a revolution that will affect the way things are done today.

In a very cientifyc way, the book shows all the aspects of how to get ready for this evolution.

The authors have written a fine book here and it has and will continue to be a source of good information on the subject. What is most interesting about the approach of genetic programming is that it does not make use of the inference methods of formal logic in the search for the correct program. Correctly observing that logical thinking is insufficient for invention and creativity, the authors follow the "logic considered harmful" philosophy in their attempts to get a computer to find a creative/original solution to a problem. And most importantly, they discuss fourteen examples where genetic programming has produced results that are competitive with human-produced results. The book is almost 1200 pages long, but without reading all the examples one could cover the main points in a reasonable time frame. The reader knowing the LISP language will appreciate the discussion more.

After a brief introduction to the book in chapter 1, the authors move on to a detailed discussion of the philosophy and approaches used in genetic programming. They list the five steps that must be done before applying a genetic algorithm to a problem and give an overview of the LISP background needed to understand genetic programming. The authors emphasize that the genetic algorithm is probabilistic in nature, with the initial populations, individual selection, and genetic operation chosen at random. They give flowcharts illustrating a typical genetic algorithm and program, and then show executable programs can be automatically created. A very extensive list of references on genetic programming is given at the end of the chapter.

In the next part, the authors discuss how to eliminate the requirement that the programmer specify the architecture in advance to the program to be created. After reviewing some methods that were previously used to make the choice of architecture, the authors move on to describing a set of architecture-altering operations that give an automated method for determining the architectures of evolving programs. The discussion on automatically defined recursion is particularly interesting.

The book then shows how to use the results so far to allow problem-solving to be done using genetic programming, the first one being the rotation of automobile tires and the second being evolving a computer program with the behavior of Boolean even-parity functions. This is followed by a discussion of how to use architecture-altering operations to solve a time-optimal control problem. The most interesting part of this discussion is that it illustrates the important point that disadvantageous actions should be taken in the short term so that the long-term objective can be achieved.

In chapter 14, the ant foraging problem is used to illustrate a form of the (Minsky) multiagent problem and architecture-altering operations. This is followed by discussions on the digit recognition problem and the transmembrane segment identification problem. The authors choose the Fibonacci sequence to illustrate how recursion can be used in solving problems with genetic programming. The necessity of using internal storage is illustrated using the cart centering problem.

The authors then overview the use of the Genetic Programming Problem Solver (GPPS) for automatically creating a computer program to solve a problem. Several problems are examined using this Solver, such as symbolic regression, sorting networks, and the intertwined spirals problem.

The next part then considers the application of genetic programming to the automated synthesis of analog electrical circuits. The authors judge, rightfully, that the design process is one that will be a good judge of automated technique versus one that was done by humans, especially considering the fact that analog design is considered by many to be an "art" rather than a "science". The authors show how to import the SPICE simulation system into the genetic programming system, and discuss how validation of circuit design using this simulator would be done by the genetic programming system. After showing how a low-pass filter may be successfully designed using the genetic programming system, the authors show how with a few changes it can be used to design many different types of circuits. Interestingly, the authors cite the rediscovery by genetic programming of the elliptic filter topology of W. Cauer. Cauer arrived at his discovery via the use of elliptic functions, but the genetic program did not make use of these, but relied solely on the problem's fitness measure and natural selection!

An interesting discussion is also given of the role of crossover in genetic programming by comparing the problem of synthesizing a lowpass filter with and without using crossover. The authors conclude that the crossover operation plays a large contribution to the actual solution of the problem.

Then later, the authors show how genetic programming actually evolved a cellular automata that performs better than a succession of algorithms written by humans in the last two decades. Specifically, they show how genetic programming evolved a rule for the majority classification problem for one-dimensional two-state cellular automata that exceeds the accuracy of all known rules.

Most interestingly, the authors show how genetic programming evolved motifs for detecting the D-E-A-D box family of proteins and for detecting the manganese superoxide dismutase family.

The actual performance and implementation issues involved in genetic programming are discussed in the last two parts of the book. They discuss the computer time needed to yield the 14 instances where they claim that genetic programming has produced results that are competitive with human-produced results.

The authors wrap things up in the last chapter of the book and discuss other instances where genetic programming has succeeded in automatically producing computer programs that are competitive with human-produced results. The evidence they have in the book is impressive but there are a few areas that will be ultimate tests of this approach, the most important being the discovery of new mathematical results or algorithms. It is this area that requires the most creativity on the part of the inventor.

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.

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.

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.

This book is based on the author's PhD dissertation and it shows (you can download the dissertation from the web). There is page after page of mind numbing step-by-step derivations that do not add too much to the discussion. I would have enjoyed the book more if Spears had shortened some of his derivations. I found the results interesting. Although some of the conclusions seem fairly obvious after reading the book, I think it is important that someone took the time to come up with the mathematical models to formalize things.

The empirical approach is very interesting, and I wish more people would follow and improve on Spears' ideas. Empirical studies of evolutionary algorithms are justly critized for being too limited to a few "standard" functions that do not show much about the capabilities and limitations of the algorithms. Spears took a good step in emulating the machine learning comunity and using test problem generators. With these generators, the experimenters can play around with parameters such as the multimodality or noise in a problem and make systematic empirical studies of the algorithms. Unfortunately, it is difficult to translate from those systematic studies to real life. For example, how much noise or how many peaks are in real-life problems?

Still, I would recommend to go and read this book (or the free dissertation). Skip the equations, though, and get to the point.

BTW, Dr Gordon (the first reviewer) is married to Spears, which may explain some of the excitement in her review...

This book is an essential resource for anyone studying the theoretical underpinnings of evolutionary algorithms (EAs). The book very carefully analyzes the effects of two fundamental evolutionary operators, recombination and mutation, and their interaction with evolutionary selection. This analysis significantly enhanced my understanding of EAs because of the fundamental role that these operators play. The book begins with the more traditional static analysis approach, but soon it transitions to a very exciting dynamic analysis. Just as neurophysiologists have discovered that when studying the brain it helps to view it as a dynamic process, Spears illustrates how much better we can understand EAs when using dynamic models, such as the popular Markov chain model approach. One of the best parts of the book was the creative use of problem generators for empirically testing the theory and for characterizing the classes of problems for which each EA operator is more effective. This was exciting for two reasons. For one, it encourages EA researchers to break away from myopic use of the same old test suites. Secondly, the problem characterization has tremendous potential value for practical applications of EAs.

Another of my favorite parts of the book was Spears' novel algorithm for compressing Markov chains. I particularly liked the mathematical analysis, which was both elegant and clear. Because Markov chains are widely used, e.g., in operations research, control theory, and artificial intelligence, this compression algorithm has wide-reaching implications for reducing the complexity of modeling a variety of systems.

The intended audience for Spears' book is computer scientists, mathematicians, and biologists, as well as students of evolutionary processes. To make the book accessible to such a diverse audience, the presentation is exceptionally clear and devoid of excessive jargon and obscure mathematics. Only an undergraduate level math background is required. One thing that I found mildly distracting was the repetition between chapters. The reason for the repetition was to make the chapters as self-sufficient as possible. Nevertheless, I read the book as a continuous whole and for anyone who does this I recommend skimming or skipping over the redunant portions. If this is done, the reader can maintain a high level of interest.

In conclusion, because of the valuable insights I gleaned from this book I believe it should be required reading for anyone who wishes to gain a better understanding of evolution as simulated by EAs. Spears' rigorous analyses and lucid explanations make this a delightful book to read.

Overall, this and the second volume combined do well to cover the major topics of evolutionary computation. Unfortunately, the IOP (the publisher) is not very good making the books (especially the first volume) available. I used both volumes for a course I teach in evolutionary computation. I am one week from the end of the semester, and most of my students received volume 2, but are STILL waiting for volume 1.

By the way, the contents of this book are available online (and free) at the IOP website, which I did not find out until the semester began.

The first volume provides a very broad coverage of the "evolutionary" literature. Reading this first volume will probably save you a lot of time. The evolutionary literature actually becomes quite large these days. The focus of this first volume is on broad coverage, not details although some chapters are already quite advanced.

If you need a fast coverage of the literature in evolutionary computation, this is the book. Pointers to all decisive contributions to the field are there. Reading from cover to cover might be difficult if the purpose is to introduce one to the field, but this is certainly the reference i would suggest to students and researchers new in this field. Each chapter is self-contained and references to the most important works for each chapter is provided at the end of the chapter.

Volume I provided the general theory of evolutionary computation. This second volume on the other hand aims at introducing the reader to more practical aspects of evolutionary computation.

While i found the first volume great, this second volume lacked the details that are required to provide an intuition of the working of advanced evolutionary techniques. I feel that "How to solve it" by Michalewicz and Fogel and "Genetic algorithms + data structures = evolution programs" by Michalewicz both provide this experience useful to implement evolutionary techniques, by not trying to trade-off pages for understandability.

I would not recommend this book because it tries to introduce advanced aspects that are too difficult to cover in a single chapter each. If you really want to understand the practice of evolutionary techniques, you need a good intuition of how the various operators and structures work on real problems, just reading a few pages will not do the job.

Honestly, I did not read it. I look forward to have it. I gave the book five star according my evaluation to the Volume 1 and my respect to David B. Fogel.

Good

The best introduction to GAs for those wishing to get up and running and using such algorithms to solve real problems. The software provided seems to work well and just about anyone will understand the book. My only complaint is that the examples could have covered a better range of topics.

Genetic Algorithms, GAs, have had a brief flowering of successful application to optimization searches and their limitations have become apparent. One consequence is that a variety of alternative evolutionary computational approaches are being investigated. Another road, much less travelled, is to examine the core mechanisms of the GA concept and try to develop a second generation of improved algorithms. This is difficult work because of the very nature of the core building block theory as first proposed by John Holland. For true inovation, building blocks must be synthesized, evaluated, and combined in sucessive hierarchies, all without external intervention. David Goldberg, a stalwart Holland desciple, has been valiantly trying to extend Holland's main theorem, which applied to infinite populations and hypthetical spaces, to finite populations on real problems.

This book is actually a research monograph reporting on the results of this research. The title "The Design of Innovation" sets up a high level of expectation but the subtitle "lessons learned from and for competent GAs" is probably right. The book offers some useful insights into the internal workings of GAs and their implication for understanding true innovation. However, despite the introductory claim of an engineering approach, the book never gets around to actually showing practitioners how to apply the lessons, nor does it give direct evidence that they work as claimed (although references to recent papers which presumably demonstrate success are given).

It is perhaps ironic that the goal for GAs has been downgraded from "universal" (as first claimed by Holland) to "competent".
Goldberg's concentrates on GAs to the exclusion of other approaches that may be equally competent or even better. A further irony is the stunning admission that "for years GA practitioners have understood that commercial applications often require" combinations of GAs and other local search methods to obtain high-quality solutions in reasonable time. But if this is so, then maybe GAs aren't the best place to start in the first place.

Goldberg's ideas about the upcoming golden age of computational innovation in the last chapter are provocative. But the implication that we must await GA improvements for this to happen are a little off-putting.

In sum, this book is a well-written research monograph intended to open up further research into the heart and soul of GAs. It should be read by researchers in AI, machine learning, and related fields. However, it will not provide the immediate answers to practitioners who are now running into the limitations of GAs (and other evolutionary or general search techniques).

Looking at the content of first 14 pages it seems that this book is going to be a very good reference for the researchers as well as beginers of the Evolutionary computing in Control. The conceptual part is also good as it can help beginers to get in to cognitive approach to the problem as tradinational methods are not useful in real time application and can now only used for comparision. Very Good Approach from the Editors.