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PARALEL MAKİNALARIN GENETİK ALGORİTMA İLE ÇİZELGELENMESİNDE MUTASYON ORANININ ETKİNLİĞİ

Year 2010, Volume: 10 Issue: 1, 199 - 210, 01.02.2010

Murat Kocamaz Ural Gökay Çiçekli

Abstract

Genetik algoritma (GA), karmaşık olarak bilinen paralel makinaların çizelgelenmesi problemlerinin çözümlenmesinde kullanılan sezgisel bir yöntemdir. GA, sahip olduğu operatörlerin gerçekleşme oranlarına bağlı olarak olumlu veya olumsuz performans göstermektedir.Bu operatörden bir tanesi de mutasyon oranıdır. Bu çalışmada paralel makinaların çizelgelenmesinde mutasyon oranının genetik algoritma performansı üzerine etkisi araştırılmıştır

Keywords

genetik algoritma mutasyon oranı

References

  • Borovska P. 2006. „Solving the Travelling Salesman Problem in Parallel by Genetic Algorithm on Multicomputer Cluster‟. International Conference on Computer Systems and Technologies - CompSysTech‟06, 15-16 June 2006, University of Veliko Tarnovo, Bulgaria
  • Gen, M. ve Cheng, R. (1997): Genetic Algorithms and Engineering Design, Wiley-IEEE.
  • Goldberg D.E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, USA.
  • Hamilton, W.D. ve Ridley, M. (2005): Narrow roads of gene land: the collected papers of W.D. Hamilton, Oxford University Press.
  • Kocamaz, M., Çiçekli, U.G. ve Soyuer, H. (2009): “A Developed Encoding Method for Parallel Machine Scheduling with Permutation Genetic Algorithm”, European and Mediterranean Conference on Information Systems EMCIS 2009, Crowne Plaza Hotel, Izmir, Brunel University and Dokuz Eylül University, 13 – 14 July 2009.
  • Mitchell, M. (1998): An Introduction to Genetic Algorithms, MIT Press.
  • MORI M. ve TSENG C.C. (1997): “Theory and Methodology: A genetic algorithm for multi-mode resource constrained project scheduling problem”, European Journal of Operational Research, 100: 134-141
  • Pinedo, M. (2008): Scheduling: Theory, Algorithms, and Systems, Edition: 3, Springer.
  • Ruiz, R. Ve Maroto, C. (2006): “A genetic algorithm for hybrid flowshops with sequence dependent setup times and machine eligibility”, European Journal of Operational Research, 169: 781–800
  • Sagegheih, A. (2006): “Scheduling problem using genetic algorithm, simulated annealing and the effects of parameter values on GA performance”, Applied Mathematical Modelling, 30: 147-154.
  • Sheikh, K. (2003): Manufacturing Resource Planning (MRP II) With an Introduction to ERP, SCM, and CRM, McGraw-Hill.
  • WANG Y.Z. (2003): “Using genetic algorithm methods to solve course scheduling problems” Expert Systems with Applications, 25: 39-50
  • YEO M. F. and Agyei E. O. (1998): “Optimising engineering problems using genetic algorithms”, Engineering Computations, 15(2): 268-280.
  • Zhenbo W. and Wenxun X. 2006. „Parallel Machine Scheduling with Special İşs‟, Tsınghua Science and Technology, 11(1): 107-110.

EFFICIENCY OF MUTATION RATE FOR PARALLEL MACHINE SCHEDULING WITH GENETIC ALGORITHM

Year 2010, Volume: 10 Issue: 1, 199 - 210, 01.02.2010

Murat Kocamaz Ural Gökay Çiçekli

Abstract

Genetic algorithms (GA) have been extensively used in parallel machine scheduling as a type of heuristic method. Depends on rate of operators, GA represents affirmative or negative performance. One of these operators is mutation rate. In this study, we addressed efficiency of mutation rate on GA for parallel machine scheduling

Keywords

genetic algorithm mutation rate

References

  • Borovska P. 2006. „Solving the Travelling Salesman Problem in Parallel by Genetic Algorithm on Multicomputer Cluster‟. International Conference on Computer Systems and Technologies - CompSysTech‟06, 15-16 June 2006, University of Veliko Tarnovo, Bulgaria
  • Gen, M. ve Cheng, R. (1997): Genetic Algorithms and Engineering Design, Wiley-IEEE.
  • Goldberg D.E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, USA.
  • Hamilton, W.D. ve Ridley, M. (2005): Narrow roads of gene land: the collected papers of W.D. Hamilton, Oxford University Press.
  • Kocamaz, M., Çiçekli, U.G. ve Soyuer, H. (2009): “A Developed Encoding Method for Parallel Machine Scheduling with Permutation Genetic Algorithm”, European and Mediterranean Conference on Information Systems EMCIS 2009, Crowne Plaza Hotel, Izmir, Brunel University and Dokuz Eylül University, 13 – 14 July 2009.
  • Mitchell, M. (1998): An Introduction to Genetic Algorithms, MIT Press.
  • MORI M. ve TSENG C.C. (1997): “Theory and Methodology: A genetic algorithm for multi-mode resource constrained project scheduling problem”, European Journal of Operational Research, 100: 134-141
  • Pinedo, M. (2008): Scheduling: Theory, Algorithms, and Systems, Edition: 3, Springer.
  • Ruiz, R. Ve Maroto, C. (2006): “A genetic algorithm for hybrid flowshops with sequence dependent setup times and machine eligibility”, European Journal of Operational Research, 169: 781–800
  • Sagegheih, A. (2006): “Scheduling problem using genetic algorithm, simulated annealing and the effects of parameter values on GA performance”, Applied Mathematical Modelling, 30: 147-154.
  • Sheikh, K. (2003): Manufacturing Resource Planning (MRP II) With an Introduction to ERP, SCM, and CRM, McGraw-Hill.
  • WANG Y.Z. (2003): “Using genetic algorithm methods to solve course scheduling problems” Expert Systems with Applications, 25: 39-50
  • YEO M. F. and Agyei E. O. (1998): “Optimising engineering problems using genetic algorithms”, Engineering Computations, 15(2): 268-280.
  • Zhenbo W. and Wenxun X. 2006. „Parallel Machine Scheduling with Special İşs‟, Tsınghua Science and Technology, 11(1): 107-110.
There are 14 citations in total.

Details

Other ID JA27YP58HG
Journal Section Research Article
Authors

Murat Kocamaz This is me

Ural Gökay Çiçekli This is me

Publication Date February 1, 2010
Published in Issue Year 2010 Volume: 10 Issue: 1

Cite

APA Kocamaz, M., & Çiçekli, U. G. (2010). EFFICIENCY OF MUTATION RATE FOR PARALLEL MACHINE SCHEDULING WITH GENETIC ALGORITHM. Ege Academic Review, 10(1), 199-210.
AMA Kocamaz M, Çiçekli UG. EFFICIENCY OF MUTATION RATE FOR PARALLEL MACHINE SCHEDULING WITH GENETIC ALGORITHM. ear. February 2010;10(1):199-210.
Chicago Kocamaz, Murat, and Ural Gökay Çiçekli. “EFFICIENCY OF MUTATION RATE FOR PARALLEL MACHINE SCHEDULING WITH GENETIC ALGORITHM”. Ege Academic Review 10, no. 1 (February 2010): 199-210.
EndNote Kocamaz M, Çiçekli UG (February 1, 2010) EFFICIENCY OF MUTATION RATE FOR PARALLEL MACHINE SCHEDULING WITH GENETIC ALGORITHM. Ege Academic Review 10 1 199–210.
IEEE M. Kocamaz and U. G. Çiçekli, “EFFICIENCY OF MUTATION RATE FOR PARALLEL MACHINE SCHEDULING WITH GENETIC ALGORITHM”, ear, vol. 10, no. 1, pp. 199–210, 2010.
ISNAD Kocamaz, Murat - Çiçekli, Ural Gökay. “EFFICIENCY OF MUTATION RATE FOR PARALLEL MACHINE SCHEDULING WITH GENETIC ALGORITHM”. Ege Academic Review 10/1 (February 2010), 199-210.
JAMA Kocamaz M, Çiçekli UG. EFFICIENCY OF MUTATION RATE FOR PARALLEL MACHINE SCHEDULING WITH GENETIC ALGORITHM. ear. 2010;10:199–210.
MLA Kocamaz, Murat and Ural Gökay Çiçekli. “EFFICIENCY OF MUTATION RATE FOR PARALLEL MACHINE SCHEDULING WITH GENETIC ALGORITHM”. Ege Academic Review, vol. 10, no. 1, 2010, pp. 199-10.
Vancouver Kocamaz M, Çiçekli UG. EFFICIENCY OF MUTATION RATE FOR PARALLEL MACHINE SCHEDULING WITH GENETIC ALGORITHM. ear. 2010;10(1):199-210.