Genetic Programming Books

It's rare to find an advanced computer science textbook that's both so engaging and so informative. I've only read the first seven chapters so far, but when I sat down to write my first genetic algorithm (for real research use), the book had already prepared me well.

It's hard to imagine a better introductory textbook for this topic.

This book is a great introduction to genetic programming and should be a model for textbook authors in other fields. Knowing little about genetic programming to begin with, this book guides the reader through the various topics and problems associated with genetic programming in a very logical and understandable way. Highly recommended! I wish more technical books were like this!

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.

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.

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.

This is a great "how to" book loaded with examples of how to implement genetic algorithms. The two main points this book makes is that many seemingly different problems can be reformulated as problems of program induction and that the genetic programming paradigm described in this book provides a way to do that program induction. No prior knowledge of conventional genetic algorithms is assumed. Thus the first three chapters are introductory material. In particular, chapter three describes the conventional genetic algorithm and introduces certain terms common to the conventional genetic algorithm and genetic programming. If you are already familiar with genetic algorithms you can skip ahead.

Chapter 4 discusses the representation problem for the conventional genetic algorithm operating on fixed-length character strings and variations of the conventional genetic algorithm dealing with structures more complex and flexible than fixed-length character strings. Since this book assumes no prior knowledge of the LISP programming language, section 4.2 describes LISP and section 4.3 outlines the reasons behind the choice of LISP for the implementation of solutions in this book. Chapter 5 provides an informal overview of the genetic programming paradigm and chapter 6 provides a detailed description of the techniques of genetic programming. Some readers may prefer to rely on chapter 5 and hold off on reading the detailed discussion in chapter 6 until they have read chapter 7 and the later chapters that contain examples.

Chapter 7 provides a detailed description of how to apply genetic programming to four introductory examples thus laying the groundwork for all of the problems to be described later in the book. Chapter 8 discusses the amount of computer processing required by the genetic programming paradigm to solve certain problems. Chapter 9 shows that the results obtained from genetic programming are not the fruits of a random search. Chapters 10 through 21 illustrate how to use genetic programming to solve a wide variety of problems from varying disciplines and are defined by the table of contents. The examples in these 12 chapters make up the heart of the book.

The final eight chapters discuss aspects of genetic algorithms common to all implementations. Chapter 22 discusses the implementation of genetic programming on parallel computer architectures. Chapter 23 discusses the ruggedness of genetic programming with respect to noise, sampling, change, and damage. Chapter 24 discusses the role of extraneous variables and functions, and chapter 25 presents the results of some experiments relating to operational issues in genetic programming. Chapter 26 summarizes the five major steps in preparing to use genetic programming while chapter 27 compares genetic programming to other machine learning paradigms. Chapter 28 is an interesting one in which the spontaneous emergence of self-replicating and self-improving computer programs is discussed. Chapter 29 attempts to wrap up the book with a conclusion.

This book is best used for its examples and practical viewpoint. There are certain matters, such as how to program in LISP, for which you will need dedicated books since the amount of detail in this book is not enough. I do highly recommend this book as a uniquely practical one on how to implement genetic algorithms via computer programs. I haven't found another with so much practical information.

If you are someone who plans to study Genetic Programming, or are already doing so and feel kind of lost about it, then you must read this book. It starts from the very top and brings you through all the steps of Genetic Programmin with tons of very useful examples.

This book is great!

Yeah, its a big book...weighs a ton. However, only the first few chapters are concerned with the basic mechanisms of GP (should be familiar to anyone with a background in genetic algorithms or evolutionary computation). The rest of the book is chock full of examples on how to apply GP. These examples are essential and very welcome. I've found that I can usually find a solved problem in Koza that is similar to what I'm after, then I adapt it to my needs. This is a great reference, but don't be fooled into thinking this book is a tutorial. Think of it more as an exposition of GP with examples. For a tutorial, look somewhere else.

I became interested in Genetic Programming after hearing one of the professors at our university lecture on it to a small group of students and other professors. I asked what book might be a good starting point and he pointed me here and i'm glad he did.

This first volume in the Genetic Programming series of books by Koza is very well organized and clear in its explanations. I have not tried the techniques presented yet, but I have some good ideas on how to proceed. The author uses LISP as the language of choice in the book, but practically any modern language should be sufficient.

If you have any interest in Genetic Programming, I encourage you to at least pick up this first volume and read through it. This technology is still relatively new and the application of the techniques seems virtually limitless.

The book was very large but enjoyable and made the subject very clear and easy to understand. It explained the genetic programming algorithm very well and showed the results of many experiments to show applicability, limitations, and characteristics of the method.

There was some repetition in places, maybe because the author wanted to emphasize some points and also to remain understandable to persons who may read selected chapters or examples rather than from cover to cover, page by page.

Although the book states that Genetic Programming does not depend on the LISP language or features, it uses LISP as its exclusive language of choice. I would like to implement these generally very computationally intensive Genetic Programming Algorithms in a very fast and efficient way, which for me implies assembly language, and although the author gives good tips about making the algorithm run faster the implementation shown is all LISP and nothing else. I am also interested in using the algorithm to generate efficient, parsimonious, code. The author described the additional problems of parsimony, but gave no information on generation of fast code from S expressions. I will have to refer to some compiler books and my own experiments to go further in this area.

I look forward to experimenting with the subject and reading some of Dr. Koza's other books on the subject.

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.

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.

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.

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.

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.

I don't really know why this book didn't sell as well as some of the other standard books in evolutionary algorithms. It's much better in many respects and presents a balanced view of the entire field, including evolution strategies, evolutionary programming, and genetic algorithms. Anyone who is interested in evolutionary algorithms should have this book....

In comparing this book with, say Goldberg's "Genetic Algorithms..." (may be the most popular genetic algorithms text), this book reads more like a German habilitation thesis (which I imagine it may have served as such), where as Goldberg's book seems more of a light introduction for the mathematically uninitiated. Indeed, Back's book seems quite scholarly with lots of useful references, and gives a good introduction to not only genetic algorithms, but also to evolutionary strategies (a paradigm that is most applicable to Euclidean-type search spaces) and evolutionary programming
(simular to ES and not to be confused with genetic programming).

I found Chapters 1 and 2 quite good, in that Chapter 1 presented the biological motivations for evolutionary computing along with a brief introduction to the theory of computation and computational complexity, while Chapter 2 gave a very good introduction to the above-mentioned evolutionary computing paradigms. The remainder of the book reads more like a report on the author's experiments in evolutionary computing.

It is important to note that Goldberg's book does not cover Evolutionary Strategies, which I have found to be a more fruitful approach since it is specifically designed for Euclidean space where many if not most interesting optimization problems are formulated in.

Finally, I offer bit of advice for those who plan to read through this book. Some of the definitions are stated with such generality that they seem very opaque upon first reading. It is very important to understand them, so do not give up! Once the defintions are understood, the algorithms will seem much easier to comprehend. In fact, the algorithms have a very simple outline:
i) initialize population
ii) while the terminating condition is not yet met: recombine to form new population members, mutate the population members, select the most fit population members to form the next generation.
The partial analyses provided for the algorithms can be skipped on first reading.

Although this book is much less popular than Goldberg's and Mitchell's, it is the most complete reference on evolutionary algorithms in my opinion. If you're looking only for an introduction to EAs, this may not be the perfect book for you (the 2 other ones are more concise) but if you're seeking a detailed review of foundations of EAs, this book is excellent. It provides mathematical insight, and examples of how to implement such algorithms.

"A Field Guide to Genetic Programming" is the best introductory book about the growing research area of Genetic Programming (GP). Written by some of the leading researchers in the field, the book explains very well the basic concepts of GP and gives a condensed state of the art of the technology. For practitioners, the book offers free Java-based software, called TinyGP and examples of many successful applications. A big benefit of the book is the comprehensive bibliography.
With its popular style and low price, "A Field Guide to Genetic Programming" can open the field to a very broad audience and create a breakthrough in GP applications.

This book is a comprehensive introduction to GP, and overview of the state of the art of the field, written by the arguably most important reserachers in the field. In other words, everything you could ask for. As a practicing evolutionary computation reserarcher, it gave me a number of new insights about the particular issues involved in evolving programs represented (mostly) as expression trees, and also a good sense of where the current big issues in the field are.

1 Introduction 1
1.1 Genetic Programming in a Nutshell . . . . . . . . . . . . . . . 2
1.2 Getting Started . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Prerequisites . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Overview of this Field Guide . . . . . . . . . . . . . . . . . . 4

Part I Basics 7

2 Representation, Initialisation and Operators in Tree-based GP 9
2.1 Representation . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 Initialising the Population . . . . . . . . . . . . . . . . . . . . 11
2.3 Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.4 Recombination and Mutation . . . . . . . . . . . . . . . . . . 15

3 Getting Ready to Run Genetic Programming 19
3.1 Step 1: Terminal Set . . . . . . . . . . . . . . . . . . . . . . . 19
3.2 Step 2: Function Set . . . . . . . . . . . . . . . . . . . . . . . 20
3.2.1 Closure . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2.2 Sufficiency . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.2.3 Evolving Structures other than Programs . . . . . . . 23
3.3 Step 3: Fitness Function . . . . . . . . . . . . . . . . . . . . . 24
3.4 Step 4: GP Parameters . . . . . . . . . . . . . . . . . . . . . 26
3.5 Step 5: Termination and solution designation . . . . . . . . . 27

4 Example Genetic Programming Run 29
4.1 Preparatory Steps . . . . . . . . . . . . . . . . . . . . . . . . 29
4.2 Step-by-Step Sample Run . . . . . . . . . . . . . . . . . . . . 31
4.2.1 Initialisation . . . . . . . . . . . . . . . . . . . . . . . 31
4.2.2 Fitness Evaluation . . . . . . . . . . . . . . . . . . . . 32
4.2.3 Selection, Crossover and Mutation . . . . . . . . . . . 32
4.2.4 Termination and Solution Designation . . . . . . . . . 35

Part II Advanced Genetic Programming 37

5 Alternative Initialisations and Operators in Tree-based GP 39
5.1 Constructing the Initial Population . . . . . . . . . . . . . . . 39
5.1.1 Uniform Initialisation . . . . . . . . . . . . . . . . . . 40
5.1.2 Initialisation may Affect Bloat . . . . . . . . . . . . . 40
5.1.3 Seeding . . . . . . . . . . . . . . . . . . . . . . . . . . 41
5.2 GP Mutation . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
5.2.1 Is Mutation Necessary? . . . . . . . . . . . . . . . . . 42
5.2.2 Mutation Cookbook . . . . . . . . . . . . . . . . . . . 42
5.3 GP Crossover . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
5.4 Other Techniques . . . . . . . . . . . . . . . . . . . . . . . . . 46

6 Modular, Grammatical and Developmental Tree-based GP 47
6.1 Evolving Modular and Hierarchical Structures . . . . . . . . . 47
6.1.1 Automatically Defined Functions . . . . . . . . . . . . 48
6.1.2 Program Architecture and Architecture-Altering . . . 50
6.2 Constraining Structures . . . . . . . . . . . . . . . . . . . . . 51
6.2.1 Enforcing Particular Structures . . . . . . . . . . . . . 52
6.2.2 Strongly Typed GP . . . . . . . . . . . . . . . . . . . 52
6.2.3 Grammar-based Constraints . . . . . . . . . . . . . . . 53
6.2.4 Constraints and Bias . . . . . . . . . . . . . . . . . . . 55
6.3 Developmental Genetic Programming . . . . . . . . . . . . . 57
6.4 Strongly Typed Autoconstructive GP with PushGP . . . . . 59

7 Linear and Graph Genetic Programming 61
7.1 Linear Genetic Programming . . . . . . . . . . . . . . . . . . 61
7.1.1 Motivations . . . . . . . . . . . . . . . . . . . . . . . . 61
7.1.2 Linear GP Representations . . . . . . . . . . . . . . . 62
7.1.3 Linear GP Operators . . . . . . . . . . . . . . . . . . . 64
7.2 Graph-Based Genetic Programming . . . . . . . . . . . . . . 65
7.2.1 Parallel Distributed GP (PDGP) . . . . . . . . . . . . 65
7.2.2 PADO . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
7.2.3 Cartesian GP . . . . . . . . . . . . . . . . . . . . . . . 67
7.2.4 Evolving Parallel Programs using Indirect Encodings . 68

8 Probabilistic Genetic Programming 69
8.1 Estimation of Distribution Algorithms . . . . . . . . . . . . . 69
8.2 Pure EDA GP . . . . . . . . . . . . . . . . . . . . . . . . . . 71
8.3 Mixing Grammars and Probabilities . . . . . . . . . . . . . . 74

9 Multi-objective Genetic Programming 75
9.1 Combining Multiple Objectives into a Scalar Fitness Function 75
9.2 Keeping the Objectives Separate . . . . . . . . . . . . . . . . 76
9.2.1 Multi-objective Bloat and Complexity Control . . . . 77
9.2.2 Other Objectives . . . . . . . . . . . . . . . . . . . . . 78
9.2.3 Non-Pareto Criteria . . . . . . . . . . . . . . . . . . . 80
9.3 Multiple Objectives via Dynamic and Staged Fitness Functions 80
9.4 Multi-objective Optimisation via Operator Bias . . . . . . . . 81

10 Fast and Distributed Genetic Programming 83
10.1 Reducing Fitness Evaluations/Increasing their Effectiveness . 83
10.2 Reducing Cost of Fitness with Caches . . . . . . . . . . . . . 86
10.3 Parallel and Distributed GP are Not Equivalent . . . . . . . . 88
10.4 Running GP on Parallel Hardware . . . . . . . . . . . . . . . 89
10.4.1 Masterslave GP . . . . . . . . . . . . . . . . . . . . . 89
10.4.2 GP Running on GPUs . . . . . . . . . . . . . . . . . . 90
10.4.3 GP on FPGAs . . . . . . . . . . . . . . . . . . . . . . 92
10.4.4 Sub-machine-code GP . . . . . . . . . . . . . . . . . . 93
10.5 Geographically Distributed GP . . . . . . . . . . . . . . . . . 93

11 GP Theory and its Applications 97
11.1 Mathematical Models . . . . . . . . . . . . . . . . . . . . . . 98
11.2 Search Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
11.3 Bloat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
11.3.1 Bloat in Theory . . . . . . . . . . . . . . . . . . . . . 101
11.3.2 Bloat Control in Practice . . . . . . . . . . . . . . . . 104

Part III Practical Genetic Programming 109

12 Applications 111
12.1 Where GP has Done Well . . . . . . . . . . . . . . . . . . . . 111
12.2 Curve Fitting, Data Modelling and Symbolic Regression . . . 113
12.3 Human Competitive Results the Humies . . . . . . . . . . . 117
12.4 Image and Signal Processing . . . . . . . . . . . . . . . . . . . 121
12.5 Financial Trading, Time Series, and Economic Modelling . . 123
12.6 Industrial Process Control . . . . . . . . . . . . . . . . . . . . 124
12.7 Medicine, Biology and Bioinformatics . . . . . . . . . . . . . 125
12.8 GP to Create Searchers and Solvers Hyper-heuristics . . . . 126
12.9 Entertainment and Computer Games . . . . . . . . . . . . . . 127
12.10The Arts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
12.11Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

13 Troubleshooting GP 131
13.1 Is there a Bug in the Code? . . . . . . . . . . . . . . . . . . . 131
13.2 Can you Trust your Results? . . . . . . . . . . . . . . . . . . 132
13.3 There are No Silver Bullets . . . . . . . . . . . . . . . . . . . 132
13.4 Small Changes can have Big Effects . . . . . . . . . . . . . . 133
13.5 Big Changes can have No Effect . . . . . . . . . . . . . . . . 133
13.6 Study your Populations . . . . . . . . . . . . . . . . . . . . . 134
13.7 Encourage Diversity . . . . . . . . . . . . . . . . . . . . . . . 136
13.8 Embrace Approximation . . . . . . . . . . . . . . . . . . . . . 137
13.9 Control Bloat . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
13.10Checkpoint Results . . . . . . . . . . . . . . . . . . . . . . . . 139
13.11Report Well . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
13.12Convince your Customers . . . . . . . . . . . . . . . . . . . . 140

14 Conclusions 141

Part IV Tricks of the Trade 143

A Resources 145
A.1 Key Books . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
A.2 Key Journals . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
A.3 Key International Meetings . . . . . . . . . . . . . . . . . . . 147
A.4 GP Implementations . . . . . . . . . . . . . . . . . . . . . . . 147
A.5 On-Line Resources . . . . . . . . . . . . . . . . . . . . . . . . 148

B TinyGP 151
B.1 Overview of TinyGP . . . . . . . . . . . . . . . . . . . . . . . 151
B.2 Input Data Files for TinyGP . . . . . . . . . . . . . . . . . . 153
B.3 Source Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
B.4 Compiling and Running TinyGP . . . . . . . . . . . . . . . . 162

Bibliography 167

Index 225

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.

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.

Introduction to Stochastic Search and Optimization provides comprehensive, current information on methods for real-world problem solving, including stochastic gradient and non-gradient techniques, as well as relatively recent innovations such as simulated annealing, genetic algorithms, and MCMC. It is written to be read and understood by graduate students, industrial practitioners, and experienced researchers in the field. Web links to software and data sets, and an extensive list of references of the book allows the reader to explore deeper into certain topic areas. I also found the index to be very comprehensive and carefully done. The appendices are as a refresher and summary of much of the prerequisite material. The book is somewhat unique in providing a balanced discussion of algorithms, including both their strengths and weaknesses. The book is among very few books that have integrated essential parts of statistical fields with optimization and decision making. The book's inclusion of a chapter on optimal experimental design is an example of such integration. The approaches discussed in the book could be used for financial decision making, forecasting, and quality improvement, among many other areas.

A must have for anyone interested in otimization! Extremely well written and objective.

Although the papers are categorized into "theory" and "applications", all papers are quite practice-oriented. The first 23 "theoretical" papers present various sub-fields of EC while still providing good entries to the applications of these sub-fields. The other 17 papers focus on the application of EC techniques on very specific problems, be they combinatorial or mostly applied.

The papers are quite well written so that reading from cover to cover is possible without getting a headache...A very good book to get an idea of the applications of EC on real problems.

A clear non-mathematical in depth description of a new idea for automatically transforming (re-engineering) existing programs to run on parallel computers. There are several low level transformations which change individual lines of code or FOR loops. Existing programs contain many lines of code so the number of possible combinations of transforms and code lines is huge. Ryan shows Darwinian natural evolution can be used to in the computer to automatically find appropriate transformations and where to apply them to the program to produce parallel code which works exactly like the original program but FASTER.

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.