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Computational Intelligence Magazine, IEEE

Issue 4 • Date Nov. 2006

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Displaying Results 1 - 25 of 25
  • IEEE Computational Intelligence Magazine - Nov. 2006

    Publication Year: 2006 , Page(s): c1
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  • IEEE Member Digital Library [advertisement]

    Publication Year: 2006 , Page(s): c2
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  • Table of contents - Vol 1 No 4

    Publication Year: 2006 , Page(s): 1
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  • Year-end review [Editor's Remarks]

    Publication Year: 2006 , Page(s): 2
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  • IEEE/CIS Publications Activities [President's Message]

    Publication Year: 2006 , Page(s): 3 - 4
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  • Call for Papers - 2007 International Joint Conference on Neural Neworks

    Publication Year: 2006 , Page(s): 5
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  • Visualizing the computational intelligence field [Application Notes]

    Publication Year: 2006 , Page(s): 6 - 10
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1719 KB) |  | HTML iconHTML  

    In this paper, we visualize the structure and the evolution of the computational intelligence (CI) field. Based on our visualizations, we analyze the way in which the CI field is divided into several subfields. The visualizations provide insight into the characteristics of each subfield and into the relations between the subfields. By comparing two visualizations, one based on data from 2002 and one based on data from 2006, we examine how the CI field has evolved over the last years. A quantitative analysis of the data further identifies a number of emerging areas within the CI field. The data that we use consist of the abstracts of the papers presented at the IEEE World Congress on Computational Intelligence (WCCI) in 2002 and 2006. Using a fully automatic procedure, so-called concept maps are constructed from the data. These maps visualize the associations between the main concepts in the CI field. Our analysis of the structure and the evolution of the CI field are largely based on the constructed concept maps View full abstract»

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  • Call for Papers - IEEE Congress on Evolutionary Computation 2007

    Publication Year: 2006 , Page(s): 11
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  • Introducing the CIS Technical Activities [Society Briefs]

    Publication Year: 2006 , Page(s): 12 - 17
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  • IEEE/CIS Tour de China [Family Corner]

    Publication Year: 2006 , Page(s): 15 - 17
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  • Foraging theory for multizone temperature control

    Publication Year: 2006 , Page(s): 18 - 27
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (4037 KB) |  | HTML iconHTML  

    Models from behavioral ecology, specifically foraging theory, are used to describe the decisions an animal forager must make in order to maximize its rate of energy gain and thereby improve its survival probability. Using a bioinspired methodology, we view an animal as a software agent, the foraging landscape as a spatial layout of temperature zones, and nutrients as errors between the desired and actual temperatures in the zones. Then, using foraging theory, we define a decision strategy for the agent that has an objective of reducing the temperature errors in order to track a desired temperature. We describe an implementation of a multizone temperature experiment, and show that the use of multiple agents defines a distributed controller that can equilibrate the temperatures in the zones in spite of interzone, ambient, and network effects. We discuss relations to ideas from theoretical ecology, and identify a number of promising research directions. It is our hope that the results of this paper will motivate other research on bioinspired methods based on behavioral ecology View full abstract»

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  • Ant colony optimization

    Publication Year: 2006 , Page(s): 28 - 39
    Cited by:  Papers (154)  |  Patents (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1238 KB) |  | HTML iconHTML  

    Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals. In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization. Ant colony optimization (ACO) takes inspiration from the foraging behavior of some ant species. These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony. Ant colony optimization exploits a similar mechanism for solving optimization problems. From the early nineties, when the first ant colony optimization algorithm was proposed, ACO attracted the attention of increasing numbers of researchers and many successful applications are now available. Moreover, a substantial corpus of theoretical results is becoming available that provides useful guidelines to researchers and practitioners in further applications of ACO. The goal of this article is to introduce ant colony optimization and to survey its most notable applications View full abstract»

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  • Advances in artificial immune systems

    Publication Year: 2006 , Page(s): 40 - 49
    Cited by:  Papers (73)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1363 KB) |  | HTML iconHTML  

    During the last decade, the field of artificial immune system (A1S) is progressing slowly and steadily as a branch of computational intelligence (CI). There has been increasing interest in the development of computational models inspired by several immunological principles. In particular, some are building models mimicking the mechanisms in the biological immune system (BIS) to better understand its natural processes and simulate its dynamical behavior in the presence of antigens/pathogens. Most of the AIS models, however, emphasize designing artifacts - computational algorithms, techniques using simplified models of various immunological processes and functionalities. Like other biologically-inspired techniques, such as artificial neural networks, genetic algorithms, and cellular automata, AISs also try to extract ideas from the BIS in order to develop computational tools for solving science and engineering problems. Although still relatively young, the artificial immune system (AIS) is emerging as an active and attractive, field involving models, techniques and applications of greater diversity View full abstract»

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  • Evolutionary computation benchmarking repository [Developmental Tools]

    Publication Year: 2006 , Page(s): 50 - 60
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (604 KB) |  | HTML iconHTML  

    Evolutionary computation has been used with great success for the solution of hard optimization problems. Theoretical analysis, although important in its own right, e.g. for understanding underlying phenomena and characteristics of evolutionary search, can only provide upper and/or lower bounds of performance estimation of evolutionary algorithms for hard optimization problems. In practice, empirical analysis is the most important means to assess and compare the performance of algorithms. In order to facilitate this fair and transparent comparison, the Evolutionary Computation Benchmarking Repository (EvoCoBR) by M. Roberts et al. (2006) has been designed and put into operation in a beta version and trial phase. The aim is to create a central Web-based repository for storing detailed benchmark problem descriptions. However, with EvoCoBR we want to go one step further and archive, along with the problem description, a list of references to previously achieved results and the best result so far. This enables researchers to more easily see how their results compare to results in the literature. EvoCoBR will also invite researchers to submit and archive the programs that produced those results. EvoCcBR's architecture enables the entire evolutionary computation community to contribute and own the Web-based archive. Its contents will be submitted by researchers and practitioners, and openly accessible by all. In other words, the EvoCoBR design defines the framework that needs to be filled by the evolutionary computation community for the evolutionary computation community View full abstract»

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  • George Friedman-evolving circuits for robots [Historic Perspective]

    Publication Year: 2006 , Page(s): 52 - 54
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (253 KB) |  | HTML iconHTML  

    Many years ago, George Friedman analyzed this problem and developed what was, in essence, a blueprint for designing an adaptive neural control circuit for mobile robots based on natural selection. Friedman's objective was to investigate the possibility of borrowing from nature to develop goal-seeking machines. He noted that in a constant environment, natural evolutionary processes lead to a series of individuals that converges on a type that is more appropriate ("better fitted") for survival View full abstract»

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  • 2007 CIS Neural Networks Pioneer Award - Michael Jordan

    Publication Year: 2006 , Page(s): 55
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  • 2007 CIS Fuzzy Systems Pioneer Award - James Keller

    Publication Year: 2006 , Page(s): 55
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  • 2007 CIS Fuzzy Systems Pioneer Award - George Klir

    Publication Year: 2006 , Page(s): 56
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  • 2007 CIS Meritorious Service Award - David B. Fogel

    Publication Year: 2006 , Page(s): 56
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  • 2007 CIS Outstanding Chapter Award

    Publication Year: 2006 , Page(s): 57
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  • Knowledge Incorporation in Evolutionary Computation [Book Review]

    Publication Year: 2006 , Page(s): 58 - 59
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  • Conference Calendar

    Publication Year: 2006 , Page(s): 60
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  • 2006 Index IEEE Computational Intelligence Magazine - Vol. 1

    Publication Year: 2006 , Page(s): 62 - 64
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  • FUZZ-IEEE 2007 - Call for Papers

    Publication Year: 2006 , Page(s): c3
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  • Introducing the IEEE Power & Energy Library

    Publication Year: 2006 , Page(s): c4
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Aims & Scope

The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications, in keeping with the Field of Interest of the IEEE Computational Intelligence Society (IEEE/CIS). 

 

Full Aims & Scope