A chromosome representation of permutations for genetic algorithms

被引:0
|
作者
Kokosinski, Z [1 ]
机构
[1] Cracow Univ Technol, Fac Elect & Comp Engn, PL-31155 Krakow, Poland
关键词
choice function; crossover operator; genetic algorithm; mutation operator; permutation chromosome;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The versatile chromosome representation of permutations is proposed ist which standard genetic operators are sufficient in order to obtain valid offspring in evolutionary computation. The representation is derived from an iterative decomposition of symmetric permutation group S-n into cosets.
引用
收藏
页码:65 / 69
页数:5
相关论文
共 50 条
  • [21] REPRESENTATION OF TREE PERMUTATIONS BY WORDS
    MAROLI, JA
    PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY, 1990, 110 (04) : 859 - 869
  • [22] Algorithms for pattern involvement in permutations
    Albert, MH
    Aldred, REL
    Atkinson, MD
    Holton, DA
    ALGORITHMS AND COMPUTATION, PROCEEDINGS, 2001, 2223 : 355 - 366
  • [23] Parallel algorithms for separable permutations
    Yugandhar, V
    Saxena, S
    DISCRETE APPLIED MATHEMATICS, 2005, 146 (03) : 343 - 364
  • [24] Some issues in chromosome codification for scheduling with genetic algorithms
    Varela, R
    Puente, J
    Vela, CR
    Planning, Scheduling and Constraint Satisfaction: From Theory to Practice, 2005, 117 : 1 - 9
  • [25] Pattern classification with genetic algorithms: Incorporation of chromosome differentiation
    Bandyopadhyay, S
    Pal, SK
    PATTERN RECOGNITION LETTERS, 1997, 18 (02) : 119 - 131
  • [26] A Propose of Genetic Operators for Quantum Algorithms with Real Representation
    Moreira Dias, Eduardo Dessupoio
    Bernardes Rebuzzi Vellasco, Marley Maria
    2021 IEEE LATIN AMERICAN CONFERENCE ON COMPUTATIONAL INTELLIGENCE (LA-CCI), 2021,
  • [27] On the Efficiency of Crossover Operators in Genetic Algorithms with Binary Representation
    Picek, Stjepan
    Golub, Marin
    RECENT ADVANCES IN NEURAL NETWORKS, FUZZY SYSTEMS & EVOLUTIONARY COMPUTING, 2010, : 167 - 172
  • [28] RGFGA: An efficient representation and crossover for grouping genetic algorithms
    Tucker, A
    Crampton, J
    Swift, S
    EVOLUTIONARY COMPUTATION, 2005, 13 (04) : 477 - 499
  • [29] A novel binary variable representation for genetic and evolutionary algorithms
    Liang, Yong
    Leung, Kwong-Sak
    Lee, Kin-Hong
    2006 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-6, 2006, : 536 - +
  • [30] Representation of permutations as products of two cycles
    Herzog, M
    Kaplan, G
    Lev, A
    DISCRETE MATHEMATICS, 2004, 285 (1-3) : 323 - 327