Analytical estimation of the convergence point of populations

Noboru Murata, Ryuei Nishii, Hideyuki Takagi, Yan Pei

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    7 Citations (Scopus)

    Abstract

    We propose methods of estimating the convergence point for the moving vectors of individuals between generations or evolution paths and show that the estimated convergence point can be useful information for accelerating evolutionary computation (EC). As the first stage of this new approach, we do not combine the proposed methods with EC search in this paper, but rather evaluate how power an individual the the estimated convergence point is by comparing fitness values. Through experimental evaluations, we show that the estimated point can be a powerful elite for unimodal fitness landscapes and that clustering moving vectors according to the aimed points is the next research target for multimodal fitness landscape.

    Original languageEnglish
    Title of host publication2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages2619-2624
    Number of pages6
    ISBN (Print)9781479974924
    DOIs
    Publication statusPublished - 2015 Sep 10
    EventIEEE Congress on Evolutionary Computation, CEC 2015 - Sendai, Japan
    Duration: 2015 May 252015 May 28

    Other

    OtherIEEE Congress on Evolutionary Computation, CEC 2015
    CountryJapan
    CitySendai
    Period15/5/2515/5/28

    Fingerprint

    Evolutionary algorithms
    Fitness Landscape
    Evolutionary Computation
    Experimental Evaluation
    Fitness
    Clustering
    Path
    Target
    Evaluate

    ASJC Scopus subject areas

    • Computer Science Applications
    • Computational Mathematics

    Cite this

    Murata, N., Nishii, R., Takagi, H., & Pei, Y. (2015). Analytical estimation of the convergence point of populations. In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings (pp. 2619-2624). [7257211] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2015.7257211

    Analytical estimation of the convergence point of populations. / Murata, Noboru; Nishii, Ryuei; Takagi, Hideyuki; Pei, Yan.

    2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 2619-2624 7257211.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Murata, N, Nishii, R, Takagi, H & Pei, Y 2015, Analytical estimation of the convergence point of populations. in 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings., 7257211, Institute of Electrical and Electronics Engineers Inc., pp. 2619-2624, IEEE Congress on Evolutionary Computation, CEC 2015, Sendai, Japan, 15/5/25. https://doi.org/10.1109/CEC.2015.7257211
    Murata N, Nishii R, Takagi H, Pei Y. Analytical estimation of the convergence point of populations. In 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 2619-2624. 7257211 https://doi.org/10.1109/CEC.2015.7257211
    Murata, Noboru ; Nishii, Ryuei ; Takagi, Hideyuki ; Pei, Yan. / Analytical estimation of the convergence point of populations. 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 2619-2624
    @inproceedings{ef406b7989d346389fc63294cbf4edce,
    title = "Analytical estimation of the convergence point of populations",
    abstract = "We propose methods of estimating the convergence point for the moving vectors of individuals between generations or evolution paths and show that the estimated convergence point can be useful information for accelerating evolutionary computation (EC). As the first stage of this new approach, we do not combine the proposed methods with EC search in this paper, but rather evaluate how power an individual the the estimated convergence point is by comparing fitness values. Through experimental evaluations, we show that the estimated point can be a powerful elite for unimodal fitness landscapes and that clustering moving vectors according to the aimed points is the next research target for multimodal fitness landscape.",
    author = "Noboru Murata and Ryuei Nishii and Hideyuki Takagi and Yan Pei",
    year = "2015",
    month = "9",
    day = "10",
    doi = "10.1109/CEC.2015.7257211",
    language = "English",
    isbn = "9781479974924",
    pages = "2619--2624",
    booktitle = "2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings",
    publisher = "Institute of Electrical and Electronics Engineers Inc.",

    }

    TY - GEN

    T1 - Analytical estimation of the convergence point of populations

    AU - Murata, Noboru

    AU - Nishii, Ryuei

    AU - Takagi, Hideyuki

    AU - Pei, Yan

    PY - 2015/9/10

    Y1 - 2015/9/10

    N2 - We propose methods of estimating the convergence point for the moving vectors of individuals between generations or evolution paths and show that the estimated convergence point can be useful information for accelerating evolutionary computation (EC). As the first stage of this new approach, we do not combine the proposed methods with EC search in this paper, but rather evaluate how power an individual the the estimated convergence point is by comparing fitness values. Through experimental evaluations, we show that the estimated point can be a powerful elite for unimodal fitness landscapes and that clustering moving vectors according to the aimed points is the next research target for multimodal fitness landscape.

    AB - We propose methods of estimating the convergence point for the moving vectors of individuals between generations or evolution paths and show that the estimated convergence point can be useful information for accelerating evolutionary computation (EC). As the first stage of this new approach, we do not combine the proposed methods with EC search in this paper, but rather evaluate how power an individual the the estimated convergence point is by comparing fitness values. Through experimental evaluations, we show that the estimated point can be a powerful elite for unimodal fitness landscapes and that clustering moving vectors according to the aimed points is the next research target for multimodal fitness landscape.

    UR - http://www.scopus.com/inward/record.url?scp=84963533752&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84963533752&partnerID=8YFLogxK

    U2 - 10.1109/CEC.2015.7257211

    DO - 10.1109/CEC.2015.7257211

    M3 - Conference contribution

    AN - SCOPUS:84963533752

    SN - 9781479974924

    SP - 2619

    EP - 2624

    BT - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

    PB - Institute of Electrical and Electronics Engineers Inc.

    ER -