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Reviews (18)

Genetic Algorithms in Search, Optimization, and Machine Learning by David E. Goldberg
Excellent book for Graduate students and instructors. Highly recommend!

Not the only paradigm for evolutionary computation
This book gives a good introduction to genetic algorithms for a general undergraduate audience. However, it is important to note that it does not cover Evolutionary Strategies, an approach to evolutionary computing that I have found quite usefulsince it is specifically designed for Euclidean space optimization problems where many if not most interesting optimization problems are formulated in (take for example the problem of determining the weights of a neural network that minimizes the network's overall classification error). Nor does it cover evolutionary programming (not to be confused with genetic programming). So after reading this book, I recommend (for the mathematically adventurous) Thomas Back's "Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms"
Read a review article instead!
I agree with another reviewer who said the book was unnecessarily long.Genetic Algorithms are a great programming tool, and there are some tips and tricks that can help your programs converge faster and more accurately, but this book had a lot of redundant information.
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Subjects:  1. Artificial Intelligence - General    2. Combinatorics    3. Computer Bks - General Information    4. Computer Books: Operating Systems    5. Computers - General Information    6. Discrete Mathematics    7. Genetic algorithms    8. Machine learning    9. Optimization (Mathematical Theory)    10. Computers / Programming / Algorithms   

Reviews (8)

READ IT BEFORE REVIEWING PLEASE
my five stars are just to counteract the single star from the idiotic reviewer who gave the book one star w/o even bothering to read it. i haven't read this volume yet either, but i just ordered it and it's on its way; i'm certain i won't be disapointed. i'm a programmer and an artist and i use GP effectively to evolve forms both sonic and visual. just because you know your field(s) doesn't mean you can't benefit from a knowledge of evolutionary algorithms, quite the contrary. we have koza and friends to thank for a lot of inspirational work. i for one enjoy the interaction that i have with my algorithms, and since i'm the fitness function, forms crop up which never would have if either i or the machine were working alone. apologies for being guilty of the same crime as said reviewer but i feel in this case it's justified.

And the future is...
Genetic programming is like a new Big Bang in computer universe. Why Should You Buy 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. ... Read more

Subjects:  1. Artificial Intelligence - General    2. Computer Books: General    3. Computers    4. Computers - Languages / Programming    5. Genetic Algorithms    6. Genetic programming (Computer    7. Genetic programming (Computer science)    8. Life Sciences - Genetics & Genomics    9. Circuits & components    10. Computers / Artificial Intelligence    11. Machine learning   

Subjects:  1. Computer Books: General    2. Computer Science    3. Computers    4. Computers - General Information    5. Finance    6. Mathematical models    7. Operations Research    8. Algorithms & procedures    9. Computer modelling & simulation    10. Computers / Computer Science    11. Investment & securities    12. ant colony systems    13. artificial immune systems    14. biologically inspired algorithms (BIAs)    15. computer trading    16. evolutionary methodologies    17. financial markets    18. financial trading    19. genetic algorithms (GAs)    20. grammatical evolution (GE)    21. multilayer perceptrons    22. neural networks (NNs)    23. particle swarm optimization (PSO)   

Reviews (1)

Good introduction book for DE
Some one who wants to beagin with DE. This the good starting point. Book started with good conceptual backgroud and carried away with codeing details of DE. Kenneth puts enough efforts to clear concept behind DE. Only thing missing is that book demands little background with GAs, EAs and optimization theory.Other wise nice book for those who are familiarized with concept of evolutionary techniques. ... Read more

Subjects:  1. Artificial Intelligence - General    2. CAD-CAM - General    3. Computer Books: General    4. Computer Science    5. Computers    6. Computers - General Information    7. Evolutionary programming (Computer science)    8. Genetic algorithms    9. Mathematical optimization    10. Algorithms    11. Computer Programming    12. Computers / Computer Science    13. Design    14. Differential evolution    15. Evolutionary computation    16. Global optimization    17. Numerical optimization    18. Optimization   

Reviews (4)

Evolution as a practical tool
The authors emphasise from the get-go that this book is meant as a practical introduction to the application of evolutionary computing. It is not a high brow, abstruse monograph. (Which indeed Springer texts often are.)
Excellent introduction
I taught our introduction to evolutionary computation class from this book.It is a well rounded introduction to the topic covering most of the introductorty material you would expect.There is an real dearth of good introductory books for EC.This is probably the bestbecause of its breadth.Its weakness is its lack of detail.It would not hurt if they covered the same material in about 50% more pages.As soon as they start a topic its over and on to the next topic.But if you are new to the field they give plenty of references and touch on most topics in enough detail for students to implement.All in all a good solid job.

An excellent textbook suitable for all levels
This is an excellent textbook which covers most aspects of the Evolutionary Computing. It's suitable for all levels. It's easy to follow, rich in content and has many references (439 to be precise) for further information. The table of contents from the book's web site is as follows:Read more

Subjects:  1. Artificial Intelligence - General    2. Computer Science    3. Computers    4. Computers - General Information    5. Evolutionary computation    6. Evolutionary programming (Comp    7. Evolutionary programming (Computer science)    8. General    9. Science/Mathematics    10. Computers / Artificial Intelligence    11. Evolutinary programming    12. Evolution strategies    13. Evolutionary computing    14. General Theory of Computing    15. Genetic algorithms    16. Genetic programming   

Subjects:  1. Applied    2. Artificial Intelligence - General    3. Computer Books: General    4. Computers    5. Life Sciences - Biology - General    6. Mathematics    7. Artificial intelligence    8. Biology, Life Sciences    9. Computing and Information Technology    10. Gene Expression Programming    11. Genetic Algorithms    12. Genetic Programming    13. Mathematics / Applied   

Subjects:  1. Artificial Intelligence - General    2. Counseling - Vocational Guidance    3. Data processing    4. Electronic Circuit Design    5. Electronic circuits    6. Electronics - Circuits - General    7. Engineering - Electrical & Electronic    8. Genetic Algorithms    9. Science/Mathematics    10. Technology    11. Technology & Industrial Arts    12. Applied mathematics    13. Electronics engineering    14. Technology / Electronics / General   

Subjects:  1. Artificial Intelligence - General    2. Automatic programming (Compute    3. Automatic programming (Computer science)    4. Computer Science    5. Computers - Languages / Programming    6. Evolutionary programming (Comp    7. Evolutionary programming (Computer science)    8. Genetic algorithms    9. Grammar    10. Language    11. Language Arts & Disciplines    12. Programming - General    13. Computer Programming    14. Computers / Artificial Intelligence    15. Machine learning   

Subjects:  1. Applied    2. Artificial Intelligence - General    3. Computer Books: Languages    4. Computers    5. Evolutionary programming (Computer science)    6. Genetic algorithms    7. Genetic programming (Computer science)    8. Mathematics    9. Programming - Algorithms    10. Algorithms & procedures    11. Bayesian Networks    12. Black-Box Optimization    13. Computers-Artificial Intelligence - General    14. Computers-Programming - Algorithms    15. Genetic and Evolutionary Algorithms    16. Hierarchical Problem Solving    17. Mathematics / Applied    18. Probabilistic Model-Building Genetic Algorithms    19. Scalable Optimization   

Subjects:  1. Algorithms (Computer Programming)    2. Artificial Intelligence - General    3. Artificial intelligence    4. Computer Engineering    5. Computers    6. Computers - General Information    7. Engineering - Electrical & Electronic    8. Engineering - Mechanical    9. Evolutionary programming (Comp    10. Evolutionary programming (Computer science)    11. Genetic algorithms    12. Programming - General    13. Science/Mathematics    14. Automatic control engineering    15. Computers / Computer Engineering    16. Software engineering   

Reviews (2)

Somewhat deceived
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. IWonderful series!
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. ... Read more

Subjects:  1. Advanced    2. Analytic Mechanics (Mathematical Aspects)    3. Artificial Intelligence - General    4. Evolutionary computation    5. Evolutionary programming (Comp    6. Evolutionary programming (Computer science)    7. General    8. Genetic Algorithms    9. Mathematics    10. Science    11. Science/Mathematics    12. Algorithms & procedures    13. Mathematics / General   

Reviews (1)

Thorough and well-organized reference
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 eachtechnique that has been used to apply genetic algorithms to the particularproblem.Descriptions include the underlying math and a step-wiseprocedure for implementation.These are backed up with multiple graphs andillustrations, and copious references (737 references in all).A few moreactual examples would have helped, especially in the MultiobjectiveOptimization chapter.The clear organization and number of references makethis an excellent reference for the practitioner, but the level of detailcould make it difficult for someone new to the technique. ... Read more

Subjects:  1. Artificial Intelligence - General    2. Computers - General Information    3. Engineering - Industrial    4. Genetic Algorithms    5. Industrial Engineering (General)    6. Industrial engineering    7. Life Sciences - Genetics & Genomics    8. Mathematical models    9. Mathematical optimization    10. Science/Mathematics    11. Technology    12. Computer aided manufacture (CAM)    13. Computers / Artificial Intelligence    14. Genetic engineering    15. Science-Life Sciences - Genetics & Genomics    16. Technology / Engineering / Mechanical    17. Technology-Engineering - Industrial   

Subjects:  1. Artificial Intelligence - General    2. Computer Bks - General Information    3. Computer Books: General    4. Computer Science    5. Computers    6. Computers - General Information    7. Logic Design    8. Adaptive Algorithms    9. Algorithms & procedures    10. Computers / Logic Design    11. Dynamic Systems    12. Evolutionary Algorithms    13. Evolutionary Programming    14. Fitness Landscapes    15. Genetic Algorithms    16. Heuristics    17. Immune Systems    18. Optimization    19. Problem Solving    20. Systems Evolution   

Subjects:  1. Artificial Intelligence - General    2. Computer Books: General    3. Computer Science    4. Computers    5. Computers - General Information    6. Logic    7. Computers / Artificial Intelligence    8. Computing and Information Technology    9. algorithmic learning    10. artificial intelligence    11. artificial neural networks    12. clustering    13. data analysis    14. data mining    15. decision making    16. evolutionary algorithms    17. fuzzy neural systems    18. fuzzy sets    19. genetic algorithms    20. image processing    21. intelligent systems    22. knowledge discovery    23. machine learning    24. multi-agent systems    25. multiple classifier systems    26. neural networks   

Reviews (1)

Research based book
For the novice reader in this field of expertise this book is well suited. It does not mean that this book is not extensively explained, but the book is easy to understand since it is going step by step through many research result examples conducted in clear way. The author intended the book to be finished within approximately few weeks of intensively reading,and some prior knowledges. The presentation is awesome and includes up-to-date notions, namely Neural Network approach, Fuzzy Logic perspective and Evolutionary Computing as well. The Evolutionary Computing contains Genetic Algorithm, Genetic Programming, Evolutionary Strategy, Evolutionary Programming and Differential Evolution. The four last notions are left in concise for some reasons. Furthermore, the Hybrid method,i.e. Neuro-Fuzzy, is explained completely by many research results exploiting the robustness of Takagi-Sugeno fuzzy inference system applied with Mackey-Glass Chaotic time series and Wang data nonlinear inputs. The most recent developments are presented in some pages including Wavelet Networks, Fractal Based Network and Fuzzy Clustering.
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Subjects:  1. Artificial Intelligence - General    2. Computer Books: General    3. Computer Science    4. Computers    5. Engineering - Electrical & Electronic    6. Probability & Statistics - General    7. Technology & Industrial Arts    8. Artificial intelligence    9. Computational Intelligence    10. Control    11. Control Applications    12. Control Engineering    13. Fuzzy Logic    14. Genetic Algorithms    15. Neural Networks    16. Probability & statistics    17. Soft Computing    18. Technology / Engineering / Electrical    19. Time Series Analysis    20. Time Series Forecasting   

Subjects:  1. Accounting - General    2. Artificial Intelligence - General    3. Business & Economics    4. Business/Economics    5. Computers - General Information    6. Data processing    7. Economics    8. Economics - General    9. Economics, Mathematical    10. Finance    11. Management Information Systems    12. Ants Algorithm    13. Artificial Intelligence    14. Business & Economics-Accounting - General    15. Business & Economics-Economics - General    16. Business Forecasting    17. Computers / Artificial Intelligence    18. Economic Modelling    19. Economic forecasting    20. Financial Data Mining    21. Financial Engineering    22. Genetic Programming    23. Neural Networks Applications    24. Pattern Matching    25. Rough Sets Theory    26. Self-Organizing Maps    27. State Space Modeling    28. Support Vector Machines    29. Time Series Modeling   

Reviews (1)

Great for GA-centered research, doubtful otherwise
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. Read more

Subjects:  1. Artificial Intelligence - General    2. Computer Books: General    3. Computer Programming    4. Computers    5. Computers - Languages / Programming    6. Evolutionary programming (Comp    7. Evolutionary programming (Computer science)    8. Genetic Algorithms    9. Programming - Algorithms    10. Programming - General    11. Computers / Computer Science    12. Evolutionary Computing    13. Machine learning   

Subjects:  1. Applied    2. Artificial Intelligence - General    3. Computational intelligence    4. Computer Books: General    5. Computer Vision    6. Computers    7. Mathematics    8. Soft computing    9. Artificial intelligence    10. Belief Networks    11. Fuzzy Logic    12. Genetic Algorithms    13. Granular Computing    14. Mathematics / Applied    15. Neural Networks   

Subjects:  1. Artificial Intelligence - General    2. Game Theory    3. Genetic Algorithms    4. Linear Programming    5. Mathematics    6. Operations Research    7. Operations Research (Engineering)    8. Science/Mathematics    9. Technology    10. Computer Programming    11. Computers / Artificial Intelligence    12. Machine learning   

Reviews (2)

Excellent reference
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. More trouble with publisher than author
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 completed the course, and most of my students received volume 2, but did not get volume 1 until well after the semester was over.
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Subjects:  1. Advanced    2. Analytic Mechanics (Mathematical Aspects)    3. Artificial Intelligence - General    4. Evolutionary computation    5. Evolutionary programming (Comp    6. Evolutionary programming (Computer science)    7. General    8. Genetic Algorithms    9. Mathematics    10. Science    11. Science/Mathematics    12. Algorithms & procedures    13. Mathematics / General