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2011 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE)

11-15 April 2011

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  • [Front cover]

    Publication Year: 2011, Page(s): c1
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  • [Copyright notice]

    Publication Year: 2011, Page(s): ii
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  • Table of contents

    Publication Year: 2011, Page(s):iii - iv
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  • IEEE CIDUE 2011 committee symposium on Computational Intelligence in dynamic and uncertain environments (IEEE CIDUE 2011)

    Publication Year: 2011, Page(s):v - vi
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  • Genetic algorithms with elitism-based immigrants for dynamic load balanced clustering problem in mobile ad hoc networks

    Publication Year: 2011, Page(s):1 - 7
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (179 KB) | HTML iconHTML

    Clustering can help aggregate the topology information and reduce the size of routing tables in a mobile ad hoc network (MANET). To achieve fairness and even energy consumption, each clusterhead should ideally support the same number of cluster members. Moreover, one of the most important characteristics in MANETs is the topology dynamics, that is, the network topology changes over time due to ene... View full abstract»

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  • An adaptive strategy for updating the memory in Evolutionary Algorithms for dynamic optimization

    Publication Year: 2011, Page(s):8 - 15
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (202 KB) | HTML iconHTML

    The memory scheme is one of the most widely employed techniques in Evolutionary Algorithms for solving dynamic optimization problems. The updating strategy is a key concern for the memory scheme. Unfortunately, the existent memory updating strategies neglect the characteristics of the memory updating behaviors, and sometimes this could lead results against the original intention. In this paper, a ... View full abstract»

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  • Trusted learner: An improved algorithm for trusted incremental function approximation

    Publication Year: 2011, Page(s):16 - 24
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (209 KB) | HTML iconHTML

    The complexity of technical systems increases drastically if they are operated in non-stationary or uncertain environments. Hence self-tuning, self-optimization and learnability get more and more important. At this as a way of achieving an intelligent system behavior, online learning systems are needed to overcome a possibly poor parameterization at engineering time and to adapt to new situations.... View full abstract»

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  • Theoretical and empirical analysis of diversity in non-stationary learning

    Publication Year: 2011, Page(s):25 - 32
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (184 KB) | HTML iconHTML

    In non-stationary learning, we require a predictive model to learn over time, adapting to changes in the concept if necessary. A major concern in any algorithm for non-stationary learning is its rate of adaptation to new concepts. When tackling such problems with ensembles, the concept of diversity appears to be of significance. In this paper, we discuss how we expect diversity to impact the rate ... View full abstract»

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  • iFAST: An Intelligent Fire-Threat Assessment and Size-up Technology for first responders

    Publication Year: 2011, Page(s):33 - 40
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (284 KB) | HTML iconHTML

    Currently, emergency response agencies use simplified “one-size-fits-all” procedures to decide what quantity and type of resources to dispatch to each fire threat. These procedures are based on principles established decades ago, and are generally static in nature. They then rely on the judgment of the experienced officer who has arrived on-scene to make a dynamic evaluation and requ... View full abstract»

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  • Hellinger distance based drift detection for nonstationary environments

    Publication Year: 2011, Page(s):41 - 48
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (853 KB) | HTML iconHTML

    Most machine learning algorithms, including many online learners, assume that the data distribution to be learned is fixed. There are many real-world problems where the distribution of the data changes as a function of time. Changes in nonstationary data distributions can significantly reduce the generalization ability of the learning algorithm on new or field data, if the algorithm is not equippe... View full abstract»

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  • Diagnosis of software erosion through fuzzy logic

    Publication Year: 2011, Page(s):49 - 56
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (317 KB) | HTML iconHTML

    Companies have a vast number of existing software systems, which are not immune to software erosion and ageing as a consequence of uncontrolled maintenance over time. Currently, there are several metrics to measure and quantify software erosion, which also recommends some maintenance actions to deal with software erosion. Unfortunately, there are many symptoms at the same time and several possible... View full abstract»

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  • Author index

    Publication Year: 2011, Page(s): 57
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