• DocumentCode
    1671856
  • Title

    Research in the Performance Assessment of Multi-objective Optimization Evolutionary Algorithms

  • Author

    Deng, Guoqiang ; Huang, Zhangcan ; Tang, Min

  • Author_Institution
    Wuhan Univ. of Technol., Wuhan
  • fYear
    2007
  • Firstpage
    915
  • Lastpage
    918
  • Abstract
    The use of evolutionary algorithms (EAs) for search and optimization tasks has become very popular in the last few years. Improving the existing algorithms or presenting new algorithms will necessarily refer to the performance assessment of these algorithms. Measuring the performance of algorithms has a basic issue: whether there exists a standard methodology that various multi-objective optimization evolutionary algorithms (MOEAs) can be directly compared. Unfortunately, researchers haven´t paid much attention to this issue. This paper reviews some of the most representative assessment methodologies used in the literature and then provides some useful suggestions and advices for researchers of algorithms.
  • Keywords
    evolutionary computation; optimisation; search problems; evolutionary algorithm; multiobjective optimization; performance assessment; Algorithm design and analysis; Benchmark testing; Binary codes; Design optimization; Evolutionary computation; Genetic algorithms; Measurement standards; Optimization methods; Search engines; System testing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communications, Circuits and Systems, 2007. ICCCAS 2007. International Conference on
  • Conference_Location
    Kokura
  • Print_ISBN
    978-1-4244-1473-4
  • Type

    conf

  • DOI
    10.1109/ICCCAS.2007.4348197
  • Filename
    4348197