English  |  正體中文  |  简体中文  |  Items with full text/Total items : 54059/88902 (61%)
Visitors : 10550479      Online Users : 12
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/21081

    Title: Dynamic programming decision path encoding of genetic algorithms for production allocation problems
    Authors: Chang, Ying-Hua;Hou, Young-Chang
    Contributors: 淡江大學資訊管理學系
    Keywords: Genetic Algorithms;Dynamic programming decision path encoding;Production allocation;Constraints satisfaction;Optimization problems
    Date: 2008-02-01
    Issue Date: 2009-11-30 13:13:12 (UTC+8)
    Publisher: Kidlington: Pergamon
    Abstract: Genetic algorithm is a novel optimization technique for solving constrained optimization problems. The penalty function methods are the popular approaches because of their simplicity and ease of implementation. Penalty encoding method needs more generations to get good solutions because it causes invalid chromosomes during evolution. In order to advance the performance of Genetic Algorithms for solving production allocation problems, this paper proposes a new encoding method, which applies the upper/lower bound concept of dynamic programming decision path on the chromosome encoding of genetic algorithm, that encodes constraints into chromosome to ensure that chromosomes are all valid during the process of evolution. Utilization of the implicated parallel processing characteristic of genetic algorithms to improve dynamic programming cannot guarantee to solve complex problems in the polynomial time. Additionally, a new simultaneous crossover and mutation operation is proposed to enable the new method to run correctly following the standard genetic algorithm procedures. This approach is evaluated on some test problems. Solutions obtained by this approach indicate that our new encoding genetic algorithms certainly accelerate the performance of the evolution process.
    Relation: Computers & Industrial Engineering 54(1), pp.53-65
    DOI: 10.1016/j.cie.2007.06.034
    Appears in Collections:[資訊管理學系暨研究所] 期刊論文

    Files in This Item:

    File Description SizeFormat
    0360-8352_54(1)p53-65.pdf495KbAdobe PDF0View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback