IEEE Transactions on Evolutionary Computation

Issue 6 • Dec. 2017

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Displaying Results 1 - 15 of 15
  • Table of contents

    Publication Year: 2017, Page(s): C1
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  • IEEE Transactions on Evolutionary Computation publication information

    Publication Year: 2017, Page(s): C2
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  • Keypoints Detection and Feature Extraction: A Dynamic Genetic Programming Approach for Evolving Rotation-Invariant Texture Image Descriptors

    Publication Year: 2017, Page(s):825 - 844
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3909 KB) | HTML iconHTML

    The goodness of the features extracted from the instances and the number of training instances are two key components in machine learning, and building an effective model is largely affected by these two factors. Acquiring a large number of training instances is very expensive in some situations such as in the medical domain. Designing a good feature set, on the other hand, is very hard and often ... View full abstract»

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  • On Partitioning Multivariate Self-Affine Time Series

    Publication Year: 2017, Page(s):845 - 862
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (989 KB)

    Given a multivariate time series, possibly of high dimension, with unknown and time-varying joint distribution, it is of interest to be able to completely partition the time series into disjoint, contiguous subseries, each of which has different distributional or pattern attributes from the preceding and succeeding subseries. An additional feature of many time series is that they display self-affi... View full abstract»

    Open Access
  • An Evolutionary Multiobjective Model and Instance Selection for Support Vector Machines With Pareto-Based Ensembles

    Publication Year: 2017, Page(s):863 - 877
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1894 KB) | HTML iconHTML

    Support vector machines (SVMs) are among the most powerful learning algorithms for classification tasks. However, these algorithms require a high computational cost during the training phase, which can limit their application on large-scale datasets. Moreover, it is known that their effectiveness highly depends on the hyper-parameters used to train the model. With the intention of dealing with the... View full abstract»

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  • A Similarity-Based Multiobjective Evolutionary Algorithm for Deployment Optimization of Near Space Communication System

    Publication Year: 2017, Page(s):878 - 897
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3903 KB) | HTML iconHTML

    The deployment of the airships plays a key role in maximizing the performance of the near space communication system. The main problem is how to strike a balance between the conflicting network speed and coverage for complex user distribution. In this paper, we propose a multiobjective deployment optimization model considering path loss, user demand, and inner structure. Under the framework of the... View full abstract»

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  • Expected Improvement of Penalty-Based Boundary Intersection for Expensive Multiobjective Optimization

    Publication Year: 2017, Page(s):898 - 913
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3975 KB) | HTML iconHTML Multimedia Media

    Computationally expensive multiobjective optimization problems are difficult to solve using solely evolutionary algorithms (EAs) and require surrogate models, such as the Kriging model. To solve such problems efficiently, we propose infill criteria for appropriately selecting multiple additional sample points for updating the Kriging model. These criteria correspond to the expected improvement of ... View full abstract»

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  • Combination of Video Change Detection Algorithms by Genetic Programming

    Publication Year: 2017, Page(s):914 - 928
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2086 KB) |  Multimedia Media

    Within the field of computer vision, change detection algorithms aim at automatically detecting significant changes occurring in a scene by analyzing the sequence of frames in a video stream. In this paper we investigate how state-of-the-art change detection algorithms can be combined and used to create a more robust algorithm leveraging their individual peculiarities. We exploited genetic program... View full abstract»

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  • DG2: A Faster and More Accurate Differential Grouping for Large-Scale Black-Box Optimization

    Publication Year: 2017, Page(s):929 - 942
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1371 KB) |  Multimedia Media

    Identification of variable interaction is essential for an efficient implementation of a divide-and-conquer algorithm for large-scale black-box optimization. In this paper, we propose an improved variant of the differential grouping (DG) algorithm, which has a better efficiency and grouping accuracy. The proposed algorithm, DG2, finds a reliable threshold value by estimating the magnitude of round... View full abstract»

    Open Access
  • A Platform That Directly Evolves Multirotor Controllers

    Publication Year: 2017, Page(s):943 - 955
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1442 KB)

    We describe an experimental platform that uses differential evolution to automatically discover high-performance multirotor controllers. All control parameters are tuned simultaneously, no modeling is required, and, as the evolution occurs on a real multirotor, the controllers are guaranteed to work in reality. The platform is able to run back-to-back experiments for over a week without human inte... View full abstract»

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  • Expected Improvement Matrix-Based Infill Criteria for Expensive Multiobjective Optimization

    Publication Year: 2017, Page(s):956 - 975
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3285 KB)

    The existing multiobjective expected improvement (EI) criteria are often computationally expensive because they are calculated using multivariate piecewise integrations, the number of which increases exponentially with the number of objectives. In order to solve this problem, this paper proposes a new approach to develop cheap-to-evaluate multiobjective EI criteria based on the proposed EI matrix ... View full abstract»

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  • Acknowledgment to Reviewers—2017

    Publication Year: 2017, Page(s):976 - 979
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  • IEEE World Congress on Computational Intelligence

    Publication Year: 2017, Page(s): 980
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  • IEEE Transactions on Evolutionary Computation Society Information

    Publication Year: 2017, Page(s): C3
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  • IEEE Transactions on Evolutionary Computation information for authors

    Publication Year: 2017, Page(s): C4
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Aims & Scope

IEEE Transactions on Evolutionary Computation publishes archival quality original papers in evolutionary computation and related areas including nature-inspired algorithms, population-based methods, and optimization where selection and variation are integral, and hybrid systems where these paradigms are combined. Purely theoretical papers are considered as are application papers that provide general insights into these areas of computation.
 

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Professor Kay Chen Tan (IEEE Fellow)
Department of Computer Science
City University of Hong Kong
Kowloon Tong, Kowloon, Hong Kong
Email: kaytan@cityu.edu.hk