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Swarm Intelligence Relié – 11 avril 2001
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Traditional methods for creating intelligent computational systems have privileged private "internal" cognitive and computational processes. In contrast, Swarm Intelligence argues that human intelligence derives from the interactions of individuals in a social world and further, that this model of intelligence can be effectively applied to artificially intelligent systems. The authors first present the foundations of this new approach through an extensive review of the critical literature in social psychology, cognitive science, and evolutionary computation. They then show in detail how these theories and models apply to a new computational intelligence methodology--particle swarms--which focuses on adaptation as the key behavior of intelligent systems. Drilling down still further, the authors describe the practical benefits of applying particle swarm optimization to a range of engineering problems. Developed by the authors, this algorithm is an extension of cellular automata and provides a powerful optimization, learning, and problem solving method.
This important book presents valuable new insights by exploring the boundaries shared by cognitive science, social psychology, artificial life, artificial intelligence, and evolutionary computation and by applying these insights to the solving of difficult engineering problems. Researchers and graduate students in any of these disciplines will find the material intriguing, provocative, and revealing as will the curious and savvy computing professional.
- Nombre de pages de l'édition imprimée544 pages
- LangueAnglais
- ÉditeurMorgan Kaufmann Publishers In
- Date de publication11 avril 2001
- Dimensions19.38 x 3.02 x 24.26 cm
- ISBN-101558605959
- ISBN-13978-1558605954
Description du produit
Biographie de l'auteur
Yuhui Shi received the Ph.D. degree in electrical engineering from Southeast University, China, in 1992. Since then, he has worked at several universities including the Department of Radio Engineering, Southeast University, Nanjing, China, the Department of Electrical & Computer Engineering, Concordia University, Montreal, Canada, the Department of Computer Science, Australian Defense Force Academic, Canberra, Australia, the Department of Computer Science, Korean Advanced Institute of Science and Technology, Taejon, Korea, and the Department of Electrical Engineering, Purdue School of Engineering and Technology, Indianapolis, Indiana, USA. He is currently with Electronic Data Systems, Inc., Kokomo, Indiana, USA, as an Applied Specialist. His main interests include artificial neural networks, evolutionary computation, fuzzy logic systems and their industrial applications.
Dr. Shi was a co-presenter of the tutorial, Introduction to Computation Intelligence, at the 1998 WCCI Conference, Anchorage, Alaska, and presented the tutorial, Evolutionary Computation and Fuzzy Systems, at the 1998 ANNIE Conference, St. Louis. He is the technical co-chair of 2001 Particle Swarm Optimization Workshop, Indianapolis, Indiana.
James Kennedy is a social psychologist who works in survey methods at the US Department of Labor. He has conducted basic and applied research into social effects on cognition and attitude. Dr. Kennedy has worked with the particle swarm computer model of social influence in artificial communities since 1994, presenting research in both the computer-science and social-science publications.
Détails sur le produit
- Éditeur : Morgan Kaufmann Publishers In; 1st ed. édition (11 avril 2001)
- Langue : Anglais
- Relié : 544 pages
- ISBN-10 : 1558605959
- ISBN-13 : 978-1558605954
- Poids de l'article : 1,05 Kilograms
- Dimensions : 19.38 x 3.02 x 24.26 cm
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PSO, itself, is deceptively simple. The heart of the algorithm can be written in a single line of code. Understanding the basis for its approach to intelligence isn't difficult, either. The authors begin their explanation using the old parable about the blind men and the elephant. You are most likely familiar with the story. In summary form, it is about a group of blind men standing around an elephant each declaring "what an elephant is like" based upon which part of the elephant they are touching -- and elephant is like: a wall (side); a tree trunk (leg); a hose (trunk); a fan (ear); and so on.
What is wrong with this story, the authors point out, is its implicit assumption that these blind men are also deaf. If not, as they each announced their impressions the individuals, as a group, would discover much more about what an elephant is. The significance here is easily missed. The capabilities of a group emerge from the individuals immersed in it. The group can do more (see more, discover more, experiment more) than the individuals from which it emerges and, by virtue of their immersion in it, the individuals benefit (and in turn, the group then benefits as it now emerges from these "benefited" individuals).
The authors view this emergent/immergent "cycle" as the driving force behind mind and intelligence. In contrast to the normal (phenomenological) view of mind as an internal, private "thing that thinks," the authors assert that mind is something requiring sociality. To put it bluntly (and the authors do), in the absence of social immersion there is no mind; mind is social. The majority of the book is focused on this: why it's true, how it's true and how it is implemented in the PSO algorithm.
It is easy to see how the book might have ended up a long philosophical argument. It isn't. Instead, the authors present a nicely written history of efforts to achieve "computational intelligence" (a much better phrase than the more familiar "artificial intelligence") including great summaries of evolutionary approaches, fuzzy logic, neural nets and artificial life. Along the way they point out recent advances in psychology and sociology. The net effect is that they don't need to argue their point. By the end of this part of the book the importance of sociality has become rather obvious. If you are interested in sociology, psychology, engineering and/or computer science you will enjoy this part of the book immensely, learn a lot and find a wealth of references to additional sources of information.
The second part of the book presents the PSO algorithm, compares its performance with other methodologies (in addition to being simpler to understand and implement, it's an order of magnitude faster when applied to certain problems -- training neural nets, for example), demonstrates how it is applied to some "real life" problems and discusses some implications of (and speculations about) the approach. As with the first part of the book, the presentation is clear, concise and informative. There is, though, indications here that the PSO approach is rather new (young). There isn't enough experience with PSO yet to give this part of the book the same feeling of depth one gets from the first part.
It's worth noting that the presentation (and description) of the PSO algorithm is done in mathematical terms. I would have much preferred a programming approach (using pseudo code) not because the math is too difficult (it's not) but because I haven't been "immersed in a mathematically minded social group" for many years. The almost exclusive use of Greek letters for symbols (variables) made reading difficult. Not only are they visually unfamiliar, I don't know their pronunciations (to illustrate the difficulty by way of analogy, consider the difference between reading "y equals b times x plus z" and "xgt equals kqj times yxf plus ktv"). I ended up rewriting the formulas in more familiar terms (using the text to figure out what the symbols represent when necessary) before I felt that I understood them.
Mentioning my problem with the math is not meant to criticize but to suggest that the book could have been made accessible to more people had it also contained a more readable (and retainable) form of the algorithm, perhaps in an appendix. A good analogy of the PSO approach (more detailed than the "blind men" story) would also have been helpful. The only real criticism I have of the book's content is a minor one. Being as it is focused on the social requirements for mind, it tends to overlook the degree of individuality required to make PSO work. The algorithm, itself, has variables which control the expression of individuality and without which it could not work (at least not well), but this flipside to the social nature of the algorithm is never discussed as such. PSO works well precisely because it maintains the rather chaotic balance between the effects of sociality and individuality. The book presents a rather one-sided view of this balance.
An aside for programmers: There is a companion site (of sorts) on the web for the book through which you can download Visual Basic and C source code of PSO implementations. There is also a Java applet which demonstrates PSO applied to a number of test functions but the source code for it is not available. There will also be an open source Java implementation as soon as I can make one available.
Particle swarm optimization is introduced in the book in both 'binary' and 'real-valued' form. The authors identify three principles behind the workings of particle swarms, namely the tendency to "evaluate"; the use of comparisons to others as a way of measuring individual status or progress; and the use of imitation. These three principles they say allow individuals to adapt to highly complex environments and solve very difficult problems. A binary decision model is used to introduce binary swarm algorithm, which is given in pseudocode, and is tested using a binary-coded version of the De Jong suite of test problems for optimization algorithms. A particle swarm model over the real numbers is then discussed, along with pseudocode, Both the binary and real models of particle swarms illustrate the fact that particle swarm optimization is a consequence of social interaction. The particles or "individuals" in the swarm learn from each other, and move to become more similar to their neighbors based on the knowledge obtained. Particle swarm optimization is dependent on the existence of social structure, the latter of which is determined by the formation of neighborhoods. These neighborhoods can have a different topology, determined solely by the numerical indices assigned to each individual.
The pseudocode given for particle swarm optimization illustrates well the basic workings of the algorithm in terms of the "local" and "global" viewpoint of the particles in the swarm. First the swarm is initialized and the performance of each particle is evaluated using its current position. The performance of each individual is then compared to its best performance so far, and the velocity for each particle changed according to a formula dependent on a system parameter. Each particle is then moved to a new position and the entire process repeated until convergence is attained. When a particle is very far from its best solution previously found, the change in velocity will be greater in order to return the particle toward its best solution. The system parameter will govern how much the particle trajectories oscillate, with smaller values of this parameter ensuring smoother trajectories. The authors give examples with graphs to illustrate this behavior and the influence of the system parameter.
Being aware that particle swarm optimization is typically viewed as a kind of evolutionary algorithm, the author address in some detail the reasons for this classification and its justification. Acknowledging that particle swarm algorithms have been influenced by evolutionary computation, they discuss some of the differences between the two approaches. In evolutionary algorithms individuals survive according to their fitness, whereas in particle swarms every individual will survive. In addition, in particle swarms, it is the velocities that are adjusted, whereas in evolutionary computing it is the positions that are state. The authors express this by saying that it is the "fate" rather than the "state" that is altered in particle swarm optimization.
The authors include an entire chapter on applications in the book, one of them being the use of particle swarms to evolve neural networks. Evolved neural networks have been shown to perform better in some cases than ones designed from scratch. After discussing some of the approaches to evolving neural networks, the authors point out, correctly, that hardly any of the studies in evolving neural networks are quantitative studies of how well they perform relative to other approaches Performance metrics are hardly ever given, which would allow interested parties to make objective and intelligent decisions on which approach is the most viable. The author's approach of using particle swarms to evolve neural networks also, interestingly, involves evolving the transfer functions of the neural networks, and they test their approach by using the Iris Data Set, a frequent benchmark for classification algorithms. Preliminary results indicate that their approach is a viable one and that it shows promise, but they admit that further experiments are needed in order to form valid conclusions.
So are the optimization algorithms based on swarm intelligence better than those that are based on, for example, on evolutionary algorithms? Are they better than those that are purely randomized algorithms? The authors are not shy about discussing how swarm intelligence optimization algorithms compare with other optimization algorithms, particularly randomized algorithms and the now famous "free-lunch" theorems of David Wolpert and William Macready. They discuss the free-lunch theorems via a very interesting example dealing with finding one's way out of a room. Using this example, they are convincing in their claim that even though no algorithm can be said to be better than any other when averaged over all cost functions, this averaging is done over processes or tasks that might be deemed absurd in the context of many problems of practical interest. Thus for "real" problems, one algorithm might indeed be "better" than another.
a) An overview of evolutionary programming techniques.
b) An exposition of the argument that intelligent behavior has a large social component in addition to a genetically determined component.
c) The presentation of an optimisation technique whereby a swarm of possible solutions fly through a problem space and base their search trajectories not only on personal experience but also on the experiences of the group. ie- There is a social component to the search of the problem space.
The presentation of (a) and (b) was quite good and readable. The presentation of (c) I found to be a little bit unclear. The algorithm is quite simple, and can be expressed succinctly, but I ended up having to go to secondary sources (web site and PSO C code) to understand exactly what they were doing. The title of the book seems to suggest the swarm develops an emergent property of intelligence. This is over-reach, and is probably not an interpretation that the authors would place on the PSO algorithm. The PSO algorithm is an interesting numeric optimisation technique, and it seems to be a more organic approach to developing neural network weights than techniques like back-propagation of errors.
Overall, a good book that I would recommend. Points off for not being clearer in explaining the algorithm details.