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Grouping Genetic Algorithms pp 67–86Cite as

Fuzzy Grouping Genetic Algorithms: Advances for Real-World Grouping Problems

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 666))

Abstract

Grouping genetic algorithm (GGA) is one of the most widely used search and optimization algorithms for grouping or clustering problems . However, the computational application of the algorithm has recently faced complex challenges. For instance, the interaction of genetic parameters’ influence and their influence on the performance of the algorithm are complex and difficult to model in a precise and explicit way. Fine-tuning, control, and adaptation of the behavior of genetic parameters and the overall GGA are difficult to describe in a precise manner. This chapter focuses on the advances and innovations in the use of fuzzy logic control concepts and their infusion in the GGA mechanism. Grouping genetic operators are enriched with fuzzy logic control and other fuzzy theoretic concepts to enable the GGA to accommodate expert opinion and guidance during its search and optimization process, and to handle complex real-world grouping problems with fuzzy characteristics. It is hoped that the proposed fuzzy GGA (FGGA) presented in this chapter is an effective and efficient algorithm for solving real-world grouping problems, even in a fuzzy environment. Avenues for further research in this direction are suggested.

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References

  • Arnone S, Dell’Orto M, Tettamanzi A (1994) Toward a fuzzy government of genetic populations. In: Proceedings of the 6th IEEE conference on tools with artificial intelligence, IEEE Computer Society Press, Los Alamitos, CA, pp 585–591

    Google Scholar 

  • Althaus E, Baumann T, Schömer E, Werth K (2007) Trunk packing revisited. LNCS 4525:420–430

    Google Scholar 

  • Bartov E, Gul FA, Tsui JSL (2000) Discretionary-accruals models and audit qualifications. J Account Econ 30(3):421–452

    Google Scholar 

  • Baker BM, Benn C (2001) Assigning Pupils to Tutor Groups in a Comprehensive School. J Oper Res Soc 52(6):623–629

    Google Scholar 

  • Bektas T (2006) The multiple traveling salesman problem: an overview of formulations and solution procedures. Omega 34(3):209–219

    Google Scholar 

  • Brandao J (2008) A deterministic tabu search algorithm for the fleet size and mix vehicle routing problem. Eur J Oper Res 195(3):716–728

    Google Scholar 

  • Chao XL, Zheng Z, Fan N, Wang XF (1999) A modified genetic algorithm by integrating neural network technology. Pattern Recog Artif Intell 12:486–492

    Google Scholar 

  • Chen Y, Fan Z-P, Ma J, Zeng S (2011) A hybrid grouping genetic algorithm for the reviewer group construction problem. Expert Syst Appl 38:2401–2411

    Google Scholar 

  • Chen AL, Martinez DH (2012) A heuristic method based on genetic algorithm for the baseline-product design. Expert Syst Appl 39(5):5829–5837

    Google Scholar 

  • Chen JC, Wu C-C, Chen C-W, Chen K-H (2012) Flexible job shop scheduling with parallel machines using Genetic Algorithm and Grouping Genetic Algorithm. Expert Syst Appl 39(2012):10016–10021

    Google Scholar 

  • Chen Y-Y (2013) Fuzzy flexible delivery and pickup problem with time windows. Information Technology and Quantitative Management (ITQM2013). Procedia Comput Sci 17:379–386

    Google Scholar 

  • Cordon O, Herrera F, Hoffmann F, Magdalena L (2001) Genetic fuzzy systems. Evolutionary tuning and learning of fuzzy knowledge bases. World Scientific

    Google Scholar 

  • Do Van P, Barros A, Bérenguer C, Bouvard K, Brissaud F (2013) Dynamic grouping maintenance with time limited opportunities. Reliab Eng Syst Safe 120:51–59

    Google Scholar 

  • Driankow D, Hellendoorn H, Reinfrank M (1993) An introduction to fuzzy control. Springer-Verlag, Berlin

    Book  Google Scholar 

  • Falkenauer E (1992) The grouping genetic algorithms—widening the scope of the GAs. Belg J Oper Res Stat Comput Sci 33:79–102

    MATH  Google Scholar 

  • Falkenauer E (1994) A new representation and operators for genetic algorithms applied to grouping problems. Evol Comput 2:123–144

    Article  Google Scholar 

  • Falkenauer E (1996) A hybrid grouping genetic algorithm for bin packing. J Heuristics 2:5–30

    Article  Google Scholar 

  • Gunn EA, Diallo C (2015) Optimal opportunistic indirect grouping of preventive replacements in multicomponent systems. Comput Ind Eng 90:281–291

    Google Scholar 

  • Henn S (2012) Algorithms for on-line order batching in an order picking warehouse. Comput Oper Res 39:2549–2563

    Google Scholar 

  • Henn S, Wäscher G (2012) Tabu search heuristics for the order batching problem in manual order picking systems. Eur J Oper Res 222:484–494

    Google Scholar 

  • Herrera F, Lozano M (2003) Fuzzy adaptive genetic algorithms: design, taxonomy, and future directions. Soft Comput 7:545–562

    Article  Google Scholar 

  • Höglund H (2013) Estimating discretionary accruals using a grouping genetic algorithm. Expert Syst Appl 40:2366–2372

    Google Scholar 

  • Hu, XB, Wu, SF (2007) Self-adaptive genetic algorithm based on fuzzy mechanism. Paper presented at the 2007 IEEE congress on evolutionary computation (CEC2007), pp 25–28

    Google Scholar 

  • Hu XB, Wu SF, Jiang J (2004) On-line free-flight path optimization based on improved genetic algorithms. Eng Appl Artif Intell 17:897–907

    Article  Google Scholar 

  • Hu XB, Paolo ED, Wu SF (2008) A comprehensive fuzz-rule-based self-adaptive genetic algorithm. Int J Intell Comput Cybern 1(1):94–109

    Article  MathSciNet  MATH  Google Scholar 

  • Joung Y-K, Noh SD (2014) Intelligent 3D packing using a grouping algorithm for automotive container engineering. J Comput Des Eng 1(2):140–151

    Google Scholar 

  • Kivelevitch E, Cohen K (2013) Manish Kumar. A market-based solution to the multiple traveling salesmen problem. J Int & Robotic Syst 72(1):21–40

    Google Scholar 

  • Li J, Kwan RSK (2001) A fuzzy simulated evolution algorithm for the driver scheduling problem. Proceedings of the 2001 IEEE Congress on Evolutionary Computation, IEEE Service Center, pp. 1115–1122

    Google Scholar 

  • Liu S, Huang W, Ma H (2009) An effective genetic algorithm for the fleet size and mix vehicle routing problems, Transport Res Part E 45:434–445

    Google Scholar 

  • Mutingi M, Mbohwa C (2014a) Home health care staff scheduling: effective grouping approaches. IAENG transactions on engineering sciences—special issue of the international multi-conference of engineers and computer scientists, IMECS 2013 and world congress on engineering, WCE 2013, CRC Press, Taylor & Francis Group, pp 215–224

    Google Scholar 

  • Mutingi M, Mbohwa C (2014b) Multi-objective homecare worker scheduling—a fuzzy simulated evolution algorithm approach. IIE Trans Health Syst Eng 4(4):209–216

    Article  Google Scholar 

  • Mutingi M, Mbohwa C (2014c) A fuzzy-based particle swarm optimization approach for task assignment in home healthcare. S Afr J Ind Eng 25(3):84–95

    Google Scholar 

  • Mutingi M, Mbohwa C (2015) Nurse scheduling: a fuzzy multi-criteria simulated metamorphosis approach. Eng Lett 23(3):222–231

    Google Scholar 

  • Onwubolu GC, Mutingi M (2001) A genetic algorithm approach to cellular manufacturing systems. Comput Ind Eng 39:125–144

    Google Scholar 

  • Phanden RK, Jain A,Verma R (2012) A genetic algorithm-based approach for job shop scheduling. J Manuf Technol Manage 23(7):937–946

    Google Scholar 

  • Rekiek B, Delchambre A, Saleh HA (2006) Handicapped Person Transportation: An application of the Grouping Genetic Algorithm. Eng Appl Artif Intell 19(5):511–520

    Google Scholar 

  • Sabuncuoglu I, Erel E, Tanyer M (2000) Assembly line balancing using genetic algorithms. J Intell Manuf 11(3):295–310

    Google Scholar 

  • Strnad D, Guid N (2010) A Fuzzy-Genetic decision support system for project team formation. Appl Soft Comput 10(4):1178–1187

    Google Scholar 

  • Tutuncu GY (2010) An interactive GRAMPS algorithm for the heterogeneous fixed fleet vehicle routing problem with and without backhauls. Eur J Oper Res 201(2):593–600

    Google Scholar 

  • Wi H, Oh S, Mun J, Jung M (2009) A team formation model based on knowledge and collaboration. Expert Syst Appl 36(5):9121–34

    Google Scholar 

  • Yu S, Yang Q, Tao J, Tian X, Yin F (2011) Product modular design incorporating life cycle issues - Group Genetic Algorithm (GGA) based method. J Cleaner Prod 19(9–10):1016–1032

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Engineering, Namibia University of Science and Technology, Windhoek, Namibia

    Michael Mutingi

  2. Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa

    Michael Mutingi & Charles Mbohwa

Authors
  1. Michael Mutingi
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  2. Charles Mbohwa
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Correspondence to Michael Mutingi .

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Mutingi, M., Mbohwa, C. (2017). Fuzzy Grouping Genetic Algorithms: Advances for Real-World Grouping Problems. In: Grouping Genetic Algorithms. Studies in Computational Intelligence, vol 666. Springer, Cham. https://doi.org/10.1007/978-3-319-44394-2_4

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  • DOI: https://doi.org/10.1007/978-3-319-44394-2_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44393-5

  • Online ISBN: 978-3-319-44394-2

  • eBook Packages: EngineeringEngineering (R0)

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