Using multiple representations in evolutionary algorithms

被引:0
|
作者
Schnier, T [1 ]
Yao, X [1 ]
机构
[1] Univ Birmingham, Sch Comp Sci, Birmingham B15 2TT, W Midlands, England
关键词
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Although evolutionary algorithms are very different from other artificial intelligence search algorithms, they face similar fundamental issues - representation and search. There have been a large amount of work done in evolutionary computation on search, such as recombination operators, mutation operators, selection schemes and various specialised operators. In comparison, research in different representations has not been as active. Most of such research has been focused on a single representation, e,g,, bit strings, real-valued vectors using cartesian coordinates, etc. This paper proposes and studies multiple representations in an evolutionary algorithm and shows empirically how multiple representations can benefit search as much as a good search operator could.
引用
收藏
页码:479 / 486
页数:8
相关论文
共 50 条
  • [41] Evolutionary algorithms for multiple criteria decision making in control
    Fleming, PJ
    Chipperfield, AJ
    NONSMOOTH AND DISCONTINUOUS PROBLEMS OF CONTROL AND OPTIMIZATION (NDPCO'98), 1999, : 227 - 234
  • [42] A novel selection mechanism for evolutionary algorithms with metameric variable-length representations
    Ryerkerk, Matt
    Averill, Ron
    Deb, Kalyanmoy
    Goodman, Erik
    SOFT COMPUTING, 2020, 24 (21) : 16439 - 16452
  • [43] A novel selection mechanism for evolutionary algorithms with metameric variable-length representations
    Matt Ryerkerk
    Ron Averill
    Kalyanmoy Deb
    Erik Goodman
    Soft Computing, 2020, 24 : 16439 - 16452
  • [44] Exploring Representations for Optimizing Connected Autonomous Vehicle Routes in Multi-Modal Transport Networks Using Evolutionary Algorithms
    Han, Kate
    Christie, Lee A.
    Zavoianu, Alexandru-Ciprian
    McCall, John A. W.
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2024, 25 (09) : 10790 - 10801
  • [45] Moving target prediction using evolutionary algorithms
    Baik, S
    Bala, J
    Hadjarian, A
    Pachowicz, P
    Baik, R
    ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS, 2005, 3501 : 217 - 221
  • [46] Evolutionary Shape Design Using Genetic Algorithms
    Tsai, Hung-Cheng
    Tseng, Sei-Wo Winger
    Tsai, Hung-Jung
    ADVANCED SCIENCE LETTERS, 2011, 4 (8-10) : 3013 - 3017
  • [47] Automated Machine Learning using Evolutionary Algorithms
    Anton, Mihai
    2020 IEEE 16TH INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTER COMMUNICATION AND PROCESSING (ICCP 2020), 2020, : 101 - 107
  • [48] Searching transcriptional modules using evolutionary algorithms
    Joung, JG
    Oh, SJ
    Zhang, BT
    PARALLEL PROBLEM SOLVING FROM NATURE - PPSN VIII, 2004, 3242 : 532 - 540
  • [49] Thresholding of Biological Images by using Evolutionary Algorithms
    Ochoa-Montiel, R.
    Sanchez-Lopez, C.
    Gonzalez-Bernal, J. A.
    2015 LATIN AMERICA CONGRESS ON COMPUTATIONAL INTELLIGENCE (LA-CCI), 2015,
  • [50] Food processing optimization using evolutionary algorithms
    Enitan, Abimbola M.
    Adeyemo, Josiah
    AFRICAN JOURNAL OF BIOTECHNOLOGY, 2011, 10 (72): : 16120 - 16127