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- Good introduction book for DE
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Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Kenneth V. Price ,
Rainer M. Storn , and
Jouni A. Lampinen
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DNA Computing: 12th International Meeting on DNA Computing, DNA12, Seoul, Korea, June 5-9, 2006, Revised Selected Papers (Lecture Notes in Computer Science)
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ASIN: 3540209506 |
Book Description
Problems demanding globally optimal solutions are ubiquitous, yet many are intractable when they involve constrained functions having many local optima and interacting, mixed-type variables. The differential evolution (DE) algorithm is a practical approach to global numerical optimization which is easy to understand, simple to implement, reliable, and fast. Packed with illustrations, computer code, new insights, and practical advice, this volume explores DE in both principle and practice. It is a valuable resource for professionals needing a proven optimizer and for students wanting an evolutionary perspective on global numerical optimization. A companion CD includes DE-based optimization software in several programming languages.
Customer Reviews:
Good introduction book for DE.......2006-03-01
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.
Book Description
Evolutionary Computing is the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. These techniques are being increasingly widely applied to a variety of problems, ranging from practical applications in industry and commerce to leading-edge scientific research.
This book presents the first complete overview of this exciting field aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. To this group the book is valuable because it presents EC as something to be used rather than just studied.
Last, but not least, this book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.
Customer Reviews:
Excellent textbook.......2006-10-31
I have used evolutionary programming in my research in the past and have read several books on the topic. This is one of the most well written books available, that can easily be read by a beginner despite its depth. The conclusions that they draw are logical and supported by the appropriate references. If you had to read only one book in evolutionary programming, consider this as a candidate.
Evolution as a practical tool.......2006-04-04
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.)
The level of discussion can be adequately understood by someone with a good background in computing and hopefully also in some science or engineering field. Certainly, there are important abstractions that must be mastered. Like how the evolutionary search can be seen as a path across a fitness landscape or potential energy surface. But there appears to be a careful explanation of the minimum necessary maths to convey an idea. And where a chapter's references might point to more specialised texts or journal papers that give a fuller math treatment.
It may well be, as another reviewer remarked, that there is insufficient detail in some passages of this book. But perhaps the text is not meant to be a low level "user's manual" type of discussion.
If you do find this book useful, consider a more advanced text, "Foundations of Genetic Programming" by Langdon and Poli, also published by Springer. It takes you deeper into the subject.
Excellent introduction.......2005-02-02
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 best because 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.......2004-06-06
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:
1. Introduction
2. What is an Evolutionary Algorithm?
3. Genetic Algorithms
4. Evolution Strategies
5. Evolutionary Programming
6. Genetic Programming
7. Learning Classifier Systems
8. Parameter Control in Evolutionary Algorithms
9. Multi-Modal Problems and Spatial Distribution
10. Hybridisation with Other Techniques: Memetic Algorithms
11. Theory
12. Constraint Handling
13. Special Forms of Evolution
14. Working with Evolutionary Algorithms
15. Summary
16. Appendices
17. Index
18. References
Recommended to everyone interested in EC.
an excellent introduction.......2004-01-29
The book is easy and refreshing to read. Assuming only a minimum of prior knowledge, all the relevant aspects are covered. The focus is on practical applications, with numerous examples, simple equations and plenty of practical advise for the user.
As should be the costum with every scientific introduction, the authors are at great pains to clarify the relationship between the different flavours of EC and to show how they historically developed.
The book does not provide much on the mathematical level, though. Not even a basic graph theoretical analysis of mutation and recombination.
This said, the book is still perfect to get you started.
Book Description
Evolutionary computation, the use of evolutionary systems as computational processes for solving complex problems, is a tool used by computer scientists and engineers who want to harness the power of evolution to build useful new artifacts, by biologists interested in developing and testing better models of natural evolutionary systems, and by artificial life scientists for designing and implementing new artificial evolutionary worlds. In this clear and comprehensive introduction to the field, Kenneth De Jong presents an integrated view of the state of the art in evolutionary computation. Although other books have described such particular areas of the field as genetic algorithms, genetic programming, evolution strategies, and evolutionary programming, Evolutionary Computation is noteworthy for considering these systems as specific instances of a more general class of evolutionary algorithms. This useful overview of a fragmented field is suitable for classroom use or as a reference for computer scientists and engineers.
Customer Reviews:
A Unified Approach-At Last!.......2007-01-10
I have read some excellent books and survey papers describing particular domains of the evolutionary computation field, but this book try to give us a "Unified Approach" of this field.
I find very interesting the author's idea to model the evolutionary process with a dynamic (nonlinear!)system so that "...given particular initial conditions the system follows a trajectory over time through a complex evolutionary state space. One can than study various aspects of these processes such as their convergence properties, their sensitivity to initial conditions, their transient behavior and so on."[pg. 2 of the book]. And, I can add, why not "chaotically behaviors".
Prof. Alexandru Serbanescu, Ph.D.,
IEEE Senior Member of CAS and Computational Intelligence Societies
how to apply biological evolution in other areas.......2006-03-12
De Jong shows how to apply the metaphor of biological evolution to many problems outside biology. He shows that in some other context, there are a few crucial issues to be determined. Firstly, what is an individual? Perhaps a particular algorithm, with certain parameter values. Then, what does it mean to have competing individuals? What is the system within which you are determining the "fitness" of an individual. Finally, how to successful individuals pass on their traits, and can these be mixed between such individuals? The latter is of course the analog of sexual reproduction.
The text describes evolutionary algorithms can be deployed as problem solvers, if you can settle the issues in the previous paragraph. These set the stage for the bulk of your computations.
Average customer rating:
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Virtual Worlds: Second International Conference, VW 2000 Paris, France, July 5-7, 2000 Proceedings (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence)
Manufacturer: Springer
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ASIN: 3540677070 |
Book Description
This book constitutes the refereed proceedings of the Second International Conference on Virtual Worlds, VW 2000, held in Paris, France, in July 2000.The 26 revised full papers presented together with two invited contributions were carefully reviewed and selected from numerous submissions. The book is divided into topical sections on virtual worlds communities and applications, virtual worlds technologies and tools, virtual humans and avatars, art and virtual worlds, artificial life and complex systems, and virtual reality and interfaces.
Book Description
Linear Genetic Programming presents a variant of genetic programming (GP) that evolves imperative computer programs as linear sequences of instructions, in contrast to the more traditional functional expressions or syntax trees. Primary characteristics of linear program structure are exploited to achieve acceleration of both execution time and evolutionary progress. Online analysis and optimization of program code lead to more efficient techniques and contribute to a better understanding of the method and its parameters. In particular, the reduction of structural variation step size and non-effective variations play a key role in finding higher quality and less complex solutions. Typical GP phenomena, such as non-effective code, neutral variations, and code growth are investigated from the perspective of linear GP.
This book serves as a reference for researchers; it also contains sufficient introductory material for students and those who are new to the field.
Book Description
Artificial Immune Systems (AIS) are adaptive systems inspired by the biological immune system and applied to problem solving. This book provides an accessible introduction that will be suitable for anyone who is beginning to study or work in this area. It gives a clear definition of an AIS, sets out the foundations of the topic (including basic algorithms), and analyses how the immune system relates to other biological systems and processes. No prior knowledge of immunology is needed - all the essential background information is covered in the introductory chapters.
Key features of the book include:
- A discussion of AIS in the context of Computational Intelligence;
- Case studies in Autonomous Navigation, Computer Network Security, Job-Shop Scheduling and Data Analysis =B7 An extensive survey of applications;
- A framework to help the reader design and understand AIS;
- A web site with additional resources including pseudocodes for immune algorithms, and links to related sites.
Written primarily for final year undergraduate and postgraduate students studying Artificial Intelligence, Evolutionary and Biologically Inspired Computing, this book will also be of interest to industrial and academic researchers working in related areas.
Customer Reviews:
Pretty good overview.......2003-06-05
Bio-inspired computing has taken the world by storm in the last few decades, going by the names of neural networks, genetic algorithms, evolutionary programming, and swarm intelligence. Another one has arisen has appeared in the last 15 years or so, is inspired by the biology of the immune system, and is the subject of this book. The authors of the book are aware that the approach is novel, but do a good job of presenting the field to newcomers (like myself), who want to know what it is all about and if it indeed has useful applications. They discuss their own work in the area and that of others, and extensive references are given for further reading.
After a short introduction to the subject in chapter 1, the authors move on to a description of the biological immune system in chapter 2. They stress the need for understanding the mechanisms that regulate the adaptive immune response, so as to be able to control the transformation of an immune response from an "aggressive" to a "benign" state. The authors explain the difference between the "innate" immune system and the "adaptive" immune system. As the name implies, the adaptive immune response is a kind of "learning" ability that allows the immune system to improve itself as antigens are encountered. The innate immune response though remains constant along the lifetime of the organism. A short description of the T-cells and B-cells is given, some of which can differentiate into "memory cells" that remain circulating in the body and protect against a given antigen. Particularly interesting is the role of "pattern recognition receptors" that recognize molecular patterns associated with pathogens. The clonal selection theory of the adaptive immune system, along with the somewhat controversial immune network theory.
Chapter 3 is an overview of how to to actually create an artificial immune system (AIS). The emphasize that anything deemed controversial in the biological framework need not be when viewed from a computational perspective, such as the immune network theory. Biology is used for the inspiration of the computational models, and as such they need not reflect entirely what is true in the biological case. They also emphasize that the various attempts to simulate the immune system on computers are not examples of an AIS. Also, an AIS is more than just a pattern recognition algorithm, even though it must employ this in its use. To give a framework for an AIS, the authors employ a model of immune cells and molecules called a "shape-space". In this shape space one models the affinity of the "molecules" via a metric, which the authors eventually choose to be the Hamming metric. They then give an overview of various algorithms for modeling the immune system, such as bone marrow, thymus, and immune network models, in addition to clonal selection algorithms. For those readers familiar with dynamical systems, the immune network models are very interesting, due to the use of differential equations, and also the fact that such in immune network models the immune system is performing even in the absence of external stimuli.
Chapter 4 gives a survey of artificial immune systems, such as spectra recognition for chemical reactions, infectious disease surveillance, analysis of medical data, and computational security. The latter was of particular importance to me, so I read the discussion and the references with more attention than other parts of the book. The issue with the approaches for network intrusion detection and virus detection lie mostly in the performance of the network. Agents that are cleverly designed may form a very accurate way of detecting this malicious behavior, but their deployment on a network may degrade the its performance considerably.
I did not read chapters 5 and 6 so I will omit their review.
In chapter 7, the authors discuss various case studies in artificial immune systems that shed more light on the examples of Chapter 4. The computer network security application is discussed again, and a low number of false positives is shown to follow after the artificial immune system is simulated. However, the performance of the network is not pointed out by the authors. The authors also give more details on the application of artificial immune systems to data analysis and optimization. The discussion is interesting, but it is still an open question as to whether this approach is indeed better than other ones in optimization theory, i.e. how does the immune approach compare with the "free-lunch" theorems so often quoted in optimization theory? The authors do make a brief comparison of their optimization algorithm with evolution strategies, and this is somewhat helpful to those who are familiar with the latter.
The last chapter of the book looks to future applications of artificial immune systems, and in its connection with learning paradigms in artificial intelligence. The authors are open-minded about the future of AIS but also subject it to critical analysis.
The book motivated me to investigate the use of AIS more fully, and to begin thinking about possible applications, such as 1. Event correlation in networks. 2. Network routing: Routes that are inefficient are viewed as "antigens", and the network immune system will then cure the system of these routes, meaning that it will remember them as being antigens up to some practical time scale. The routing scheme in place will not implement these routes within this time frame. 3. The TCP/IP protocol in the context of the immune network theory where reliable connections are based on the epitope/paratope recognition capability. Any emergent properties of the network overlaid with the TCP/IP protocol such as learning, memory, and self-tolerance could be studied by viewing the packet network as an immune network. 4. Network QoS, with packets marked as low priority viewed as temporary antigens. 5. Using the function optimization capabilities of AIS do calculate the effective bandwidth of ATM networks. 6. Data analysis, particularly in the construction of algorithms to distinguish chaos from noise.
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Evolutionary Computation in Data Mining (Studies in Fuzziness and Soft Computing)
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ASIN: 3540223703 |
Book Description
This carefully edited book reflects and advances the state of the art in the area of Data Mining and Knowledge Discovery with Evolutionary Algorithms. It emphasizes the utility of different evolutionary computing tools to various facets of knowledge discovery from databases, ranging from theoretical analysis to real-life applications.
Evolutionary Computation in Data Mining provides a balanced mixture of theory, algorithms and applications in a cohesive manner, and demonstrates how the different tools of evolutionary computation can be used for solving real-life problems in data mining and bioinformatics.
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Applications Of Multi-Objective Evolutionary Algorithms (Advances in Natural Computation)
Manufacturer: World Scientific Publishing Company
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ASIN: 9812561064 |
Book Description
This book presents an extensive variety of multi-objective problems across diverse disciplines, along with statistical solutions using multi-objective evolutionary algorithms (MOEAs). The topics discussed serve to promote a wider understanding as well as the use of MOEAs, the aim being to find good solutions for high-dimensional real-world design applications. The book contains a large collection of MOEA applications from many researchers, and thus provides the practitioner with detailed algorithmic direction to achieve good results in their selected problem domain.
Book Description
Evolutionary Computation 2: Advanced Algorithms and Operators expands upon the basic ideas underlying evolutionary algorithms. The focus is on fitness evaluation, constraint-handling techniques, population structures, advanced techniques in evolutionary computation, and the implementation of evolutionary algorithms. It is intended to be used by individual researchers and students in the expanding field of evolutionary computation.
Customer Reviews:
Somewhat deceived.......2003-05-12
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.
IWonderful series!.......2001-02-02
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.
Book Description
One of the main difficulties of applying an evolutionary algorithm (or, as a matter of fact, any heuristic method) to a given problem is to decide on an appropriate set of parameter values. Typically these are specified before the algorithm is run and include population size, selection rate, operator probabilities, not to mention the representation and the operators themselves. This book gives the reader a solid perspective on the different approaches that have been proposed to automate control of these parameters as well as understanding their interactions. The book covers a broad area of evolutionary computation, including genetic algorithms, evolution strategies, genetic programming, estimation of distribution algorithms, and also discusses the issues of specific parameters used in parallel implementations, multi-objective evolutionary algorithms, and practical consideration for real-world applications. It is a recommended read for researchers and practitioners of evolutionary computation and heuristic methods.
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