• DocumentCode
    3590472
  • Title

    Competitive advantages of evolutionary computation for industrial applications

  • Author

    Kordon, A.K. ; Kalos, A.N. ; Castillo, F.A. ; Jordaan, E.M. ; Smits, G.F. ; Kotanchek, M.E.

  • Author_Institution
    The Dow Chem. Co., Freeport, TX
  • Volume
    1
  • fYear
    2005
  • Firstpage
    166
  • Abstract
    Defining the technical and business competitive advantages of evolutionary computation (EC) is critical for successful marketing of this technology in industry and other research communities. The key competitive advantages of EC, based on industrial applications in the chemical industry are presented in the paper. Gaining competitive advantage by integrating EC with statistical methods, neural networks, and support vector machines is recommended. Several examples of application areas in the chemical industry with demonstrated competitive advantage of EC are given. The most important areas are inferential sensors, empirical emulators of mechanistic models, accelerated new product development, complex process optimization, and effective industrial design of experiments
  • Keywords
    chemical industry; evolutionary computation; neural nets; optimisation; statistical analysis; support vector machines; chemical industry; empirical emulators; evolutionary computation; industrial experiment design; inferential sensors; mechanistic models; neural networks; process optimization; product development; statistical methods; support vector machines; Acceleration; Chemical industry; Chemical sensors; Chemical technology; Computer industry; Evolutionary computation; Neural networks; Product development; Statistical analysis; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Evolutionary Computation, 2005. The 2005 IEEE Congress on
  • Print_ISBN
    0-7803-9363-5
  • Type

    conf

  • DOI
    10.1109/CEC.2005.1554681
  • Filename
    1554681