A general steady state distribution based stopping criteria for finite length genetic algorithms

被引：20
|
作者：
Pendharkar, Parag C.
Koehler, Gary J.
机构：
[1] Penn State Univ, Capital Coll, Sch Business Adm, Middletown, PA 17057 USA
[2] Univ Florida, Warrington Coll Business Adm, Gainesville, FL 32611 USA
来源：
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH | 2007年 / 176卷 / 03期
关键词：
genetic algorithms; heuristic search; worst-case analysis; error bounds; Markov processes;
D O I：
10.1016/j.ejor.2005.10.050
中图分类号：
C93 [管理学];
学科分类号：
12 ; 1201 ; 1202 ; 120202 ;
摘要：
We propose two general stopping criteria for finite length, simple genetic algorithms based on steady state distributions, and empirically investigate the impact of mutation rate, string length, crossover rate and population size on their convergence. Our first stopping criterion is based on the second largest eigenvalue of the genetic algorithm transition matrix, and the second stopping criterion is based on minorization conditions. (c) 2005 Elsevier B.V. All rights reserved.
引用
下载
收藏
页码：1436 / 1451
页数：16
相关论文
共 50 条
  • [1] On stopping criteria for genetic algorithms
    Safe, M
    Carballido, J
    Ponzoni, I
    Brignole, N
    ADVANCES IN ARTIFICIAL INTELLIGENCE - SBIA 2004, 2004, 3171 : 405 - 413
  • [2] Stopping Criteria for Genetic Algorithms with Application to Multiobjective Optimization
    Studniarski, Marcin
    PARALLEL PROBLEMS SOLVING FROM NATURE - PPSN XI, PT I, 2010, 6238 : 697 - 706
  • [3] Stopping criteria for averaging the multiple auditory steady-state response
    Torres Fortuny, Alejandro
    Perez Abalo, Maria C.
    Sotero Diaz, Roberto C.
    Rioja Rodriguez, Lilliam
    Rodriguez Davila, Ernesto
    Galan Garcia, Lidice
    Eimil Suarez, Eduardo
    ACTA OTORRINOLARINGOLOGICA ESPANOLA, 2011, 62 (03): : 173 - 180
  • [4] Accelerating Steady-State Genetic Algorithms based on CUDA Architecture
    Oiso, Masashi
    Yasuda, Toshiyuki
    Ohkura, Kazuhiro
    Matumura, Yoshiyuki
    2011 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2011, : 687 - 692
  • [5] Theoretical analysis of steady state genetic algorithms
    Alexandru Agapie
    Alden H. Wright
    Applications of Mathematics, 2014, 59 : 509 - 525
  • [6] Theoretical analysis of steady state genetic algorithms
    Agapie, Alexandru
    Wright, Alden H.
    APPLICATIONS OF MATHEMATICS, 2014, 59 (05) : 509 - 525
  • [7] Genetic algorithms aid steady-state modeling
    Rakestraw, Roy
    Pipe Line Industry, 1991, 74 (05):
  • [8] Comparison of steady state and elitist selection genetic algorithms
    Shi, Z
    Cui, ZL
    Zhou, Y
    PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON INTELLIGENT MECHATRONICS AND AUTOMATION, 2004, : 495 - 499
  • [9] Reducing genetic drift in steady state evolutionary algorithms
    Branke, J
    Cutaia, M
    Dold, H
    GECCO-99: PROCEEDINGS OF THE GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 1999, : 68 - 74
  • [10] Reinforcement learning in steady-state genetic algorithms
    Lee, CY
    Antonsson, EK
    CEC'02: PROCEEDINGS OF THE 2002 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2, 2002, : 1793 - 1797
12345