A Genetic Algorithm with Local Search for Solving Job Problems

  • L W Cai
  • Q H Wu
  • Z Z Yong
Conference paper

DOI: 10.1007/3-540-45561-2_11

Part of the Lecture Notes in Computer Science book series (LNCS, volume 1803)
Cite this paper as:
Cai L.W., Wu Q.H., Yong Z.Z. (2000) A Genetic Algorithm with Local Search for Solving Job Problems. In: Cagnoni S. (eds) Real-World Applications of Evolutionary Computing. EvoWorkshops 2000. Lecture Notes in Computer Science, vol 1803. Springer, Berlin, Heidelberg

Abstract

This paper presents a genetic algorithm specially designed for job shop problems. The algorithm has a simple coding scheme and new crossover and mutation operators. A simple local search scheme is incorporated in the algorithm leading to a combined genetic algorithm(CGA). It is evaluated in three famous Muth and Thompson problems (i.e. MT6×6, MT10×10, MT20×5). The simulation study shows that this algorithm possesses high efficiency and is able to find out the optimal solutions for the job shop problems.

Keywords

Combined genetic algorithm local search job shop scheduling 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • L W Cai
    • 1
  • Q H Wu
    • 2
  • Z Z Yong
    • 1
  1. 1.Department of Electronic EngineeringShenzhen UniversityShenzhenP. R. China
  2. 2.Department of Electrical Engineering and ElectronicsThe University of LiverpoolLiverpoolUK

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