• DocumentCode
    3631592
  • Title

    A study on performance of MOGA and HLCGA for the Linear Ordering Problem

  • Author

    Pavel Kromer;Jan Platos;Vaclav Snasel

  • Author_Institution
    Department of Computer Science, Faculty of Electrical Engineering and Computer Science, V?B - Technical University of Ostrava, 17. listopadu 15, 708 33 Poruba, Czech Republic
  • fYear
    2008
  • fDate
    6/1/2008 12:00:00 AM
  • Firstpage
    399
  • Lastpage
    404
  • Abstract
    Linear Ordering Problem (LOP) is a well know optimization problem attractive for its complexity (it is a NP-hard problem), rich collection of testing data and variety of real world applications. In this paper, we investigate the usage and performance of two variants of Genetic Algorithms - Mutation Only Genetic Algorithms and Higher Level Chromosome Genetic Algorithms - on the Linear Ordering Problem. Both methods are tested and evaluated on a collection of real world and artificial LOP instances.
  • Keywords
    "Genetic algorithms","Optimization methods","Biological cells","Computer science","Benchmark testing","Evolutionary computation","Encoding","NP-hard problem","Job shop scheduling","Libraries"
  • Publisher
    ieee
  • Conference_Titel
    Soft Computing in Industrial Applications, 2008. SMCia ´08. IEEE Conference on
  • Print_ISBN
    978-1-4244-3782-5
  • Type

    conf

  • DOI
    10.1109/SMCIA.2008.5045997
  • Filename
    5045997
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3631592