Abstract
The job-shop scheduling problem (JSSP) is a well known di.cult NP-hard problem. Genetic Algorithms (GAs) for solving the JSSP have been proposed, and they perform well compared with other approaches [1]. However, the tuning of genetic parameters has to be performed by trial and error. To address this problem, Sawai et al. have proposed the Parameter-free GA (PfGA), for which no control parameters for genetic operation need to be set in advance [3].
We proposed an extension of the PfGA, a real-coded PfGA, for JSSP [2], and reported that the GA performed well without tedious parameter-tuning. This paper reports the performance of the GA to a wider range of problem instances. The simulation results show that the GA performs well for many problem instances, and the performance can be improved greatly by increasing the number of subpopulations in the parallel distributed version.
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Jain, A.S., Meeran, S.: Deterministic job-shop scheduling: past, present and future. European Journal of Operational Research 113, 390–434 (1999)
Matsui, S., Watanabe, I., Tokoro, K.: Real-coded parameter-free genetic algorithm for job-shop scheduling problems. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 800–810. Springer, Heidelberg (2002)
Sawai, H., Kizu, S.: Parameter-free genetic algorithm inspired by “disparity theory of evolution”. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 702–711. Springer, Heidelberg (1998)
Vaessens, R.J.M.: Operations Research Library of Problems, Management School, Imperial College London (1996), ftp://mscmga.ms.ic.ac.uk/pub/jobshop1.txt
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Matsui, S., Watanabe, I., Tokoro, Ki. (2004). Empirical Performance Evaluation of a Parameter-Free GA for JSSP. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_145
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DOI: https://doi.org/10.1007/978-3-540-24855-2_145
Publisher Name: Springer, Berlin, Heidelberg
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