Population-Based Algorithm with Selectable Evolutionary Operators for Nonlinear Modeling

被引：0
|
作者：
Lapa, Krystian [1 ]
机构：
[1] Czestochowa Tech Univ, Inst Computat Intelligence, Czestochowa, Poland
来源：
INFORMATION SYSTEMS ARCHITECTURE AND TECHNOLOGY, PT I | 2018年 / 655卷
关键词：
Population-based algorithms; Fuzzy systems; Nonlinear modeling; Selection of evolutionary operators;
D O I：
10.1007/978-3-319-67220-5_2
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper a new population-based algorithm for nonlinear modeling is proposed. Its advantage is the automatic selection of evolutionary operators and their parameters for individuals in population. In this approach evolutionary operators are selected from a large set of operators, however only the solutions that use low number of operators are promoted in population. Moreover, assigned operators can be changed during evolution of population. Such approach: (a) eliminates the need for determining detailed mechanism of the population-based algorithm, and (b) reduces the complexity of the algorithm. For the simulations typical nonlinear modeling benchmarks are used.
引用
收藏
页码：15 / 26
页数：12
相关论文
共 50 条
  • [31] Multi Operators-based Partial Connected Parallel Evolutionary Algorithm
    Laili, Yuanjun
    Tao, Fet
    Zhang, Lin
    2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2016, : 4289 - 4296
  • [32] Analysis of Population-based Evolutionary Algorithms for the Vertex Cover Problem
    Oliveto, Pietro S.
    He, Jun
    Yao, Xin
    2008 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-8, 2008, : 1563 - 1570
  • [33] ESDL: A Simple Description Language for Population-Based Evolutionary Computation
    Dower, Steve
    Woodward, Clinton J.
    GECCO-2011: PROCEEDINGS OF THE 13TH ANNUAL GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2011, : 1045 - 1052
  • [34] Automatic deep spare clustering with a dynamic population-based evolutionary algorithm using reinforcement learning and transfer learning
    Hadikhani, Parham
    Lai, Daphne Teck Ching
    Ong, Wee-Hong
    Nadimi-Shahraki, Mohammad H.
    IMAGE AND VISION COMPUTING, 2024, 151
  • [35] Construction of projection operators for nonlinear evolutionary dynamics
    Degenhard, A
    Rodríguez-Laguna, J
    MULTISCALE MODELING & SIMULATION, 2005, 4 (02): : 641 - 663
  • [36] A Short Survey on Population-Based Incremental Learning Algorithm
    Folly, Komla A.
    2019 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2019), 2019, : 339 - 344
  • [37] Parallel population-based algorithm portfolios: An empirical study
    Akay, Rustu
    Basturk, Alper
    Kalinli, Adem
    Yao, Xin
    NEUROCOMPUTING, 2017, 247 : 115 - 125
  • [38] A population-based incremental learning algorithm with elitist strategy
    Zhang, Qingbin
    Wu, Tihua
    Liu, Bo
    ICNC 2007: THIRD INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 3, PROCEEDINGS, 2007, : 583 - +
  • [39] A diversity maintaining population-based incremental learning algorithm
    Ventresca, Mario
    Tizhoosh, Hamid R.
    INFORMATION SCIENCES, 2008, 178 (21) : 4038 - 4056
  • [40] From an individual to a population: An analysis of the first hitting time of population-based evolutionary algorithms
    He, J
    Yao, X
    IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (05) : 495 - 511
12345