Differential evolution with an evolution path : a DEEP evolutionary algorithm

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Li, Yuan-Long and Zhan, Zhi-Hui and Gong, Yue-Jiao and Chen, Wei-Neng and Zhang, Jun and Li, Yun (2015) Differential evolution with an evolution path : a DEEP evolutionary algorithm. IEEE Transactions on Cybernetics, 45 (9). pp. 1798-1810. ISSN 2168-2275 (https://doi.org/10.1109/TCYB.2014.2360752)

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Abstract

Utilizing cumulative correlation information already existing in an evolutionary process, this paper proposes a predictive approach to the reproduction mechanism of new individuals for differential evolution (DE) algorithms. DE uses a distributed model (DM) to generate new individuals, which is relatively explorative, whilst evolution strategy (ES) uses a centralized model (CM) to generate offspring, which through adaptation retains a convergence momentum. This paper adopts a key feature in the CM of a covariance matrix adaptation ES, the cumulatively learned evolution path (EP), to formulate a new evolutionary algorithm (EA) framework, termed DEEP, standing for DE with an EP. Without mechanistically combining two CM and DM based algorithms together, the DEEP framework offers advantages of both a DM and a CM and hence substantially enhances performance. Under this architecture, a self-adaptation mechanism can be built inherently in a DEEP algorithm, easing the task of predetermining algorithm control parameters. Two DEEP variants are developed and illustrated in the paper. Experiments on the CEC'13 test suites and two practical problems demonstrate that the DEEP algorithms offer promising results, compared with the original DEs and other relevant state-of-the-art EAs.

ORCID iDs

Li, Yuan-Long, Zhan, Zhi-Hui, Gong, Yue-Jiao, Chen, Wei-Neng, Zhang, Jun and Li, Yun ORCID logoORCID: https://orcid.org/0000-0002-6575-1839;
  • Item type:Article
    ID code:65166
    Dates:
    Date
    Event
    30 September 2015
    Published
    9 October 2014
    Published Online
    16 September 2014
    Accepted
    Notes:© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering
    Depositing user: Pure Administrator
    Date deposited:14 Aug 2018 15:46
    Last modified:17 Dec 2024 16:35
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/65166
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