Journal of Intelligent Manufacturing

, Volume 14, Issue 3–4, pp 323–339 | Cite as

Finding multiple solutions in job shop scheduling by niching genetic algorithms

  • E. Pérez
  • F. Herrera
  • C. Hernández


The interest in multimodal optimization methods is increasing in the last years. The objective is to find multiple solutions that allow the expert to choose the solution that better adapts to the actual conditions. Niching methods extend genetic algorithms to domains that require the identification of multiple solutions. There are different niching genetic algorithms: sharing, clearing, crowding and sequential, etc. The aim of this study is to study the applicability and the behavior of several niching genetic algorithms in solving job shop scheduling problems, by establishing a criterion in the use of different methods according to the needs of the expert. We will experiment with different instances of this problem, analyzing the behavior of the algorithms from the efficacy and diversity points of view.

Job shop scheduling problem multimodal optimization genetic algorithms niching methods 


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Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • E. Pérez
    • 1
  • F. Herrera
    • 2
  • C. Hernández
    • 1
  1. 1.Industrial Engineering Group, School of Industrial EngineeringUniversity of ValladolidValladolidSpain
  2. 2.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

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