Skip to main content
SpringerLink
Log in

Grouping Genetic Algorithms: Advances for Real-World Grouping Problems

  • Chapter
  • First Online:
Grouping Genetic Algorithms

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

  • 1317 Accesses

Abstract

Following the inception of the grouping genetic algorithm (GGA) in the 1990s, research activities on GGA techniques and their applications on real-world grouping problems have continued to grow. The number of new grouping problems continues to grow in the literature. Not surprisingly, the complexity of the problems continues to grow as well. As the size and complexity of the problems continue to grow, developing advanced genetic techniques is imperative. The focus of this chapter is to provide an outline of the recent advances on GGA techniques, their strengths, weaknesses, and potential areas of application. In addition, new techniques and developments are proposed, including their strengths and potential areas of application. The potential of these techniques on improving the performance of the GGA procedure is quite promising.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Agustın-Blas, LE, Salcedo-Sanz S, Ortiz-Garcıa EG, Portilla-Figueras A, Pérez-Bellido AM, JimJiménez-Fernández S (2011) Team formation based on group technology: a hybrid grouping genetic algorithm approach. Comput Oper Res 38: 484–495

    Google Scholar 

  • Caetano SS, Ferreira DJ, Camilo CG (2013) Multi-objective genetic algorithm for competency-based selection of auditing teams. J Softw Syst Dev Article ID 369217:1–13. doi:10.5171/2013.369217

    Article  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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Chen R-C, Chen S-Y, Fan J-Y, Chen Y-T (2012) Grouping partners for cooperative learning using genetic algorithm and social network analysis. In: The 2012 international workshop on information and electronics engineering (IWIEE). Procedia Engineering, vol 29, pp. 3888–3893

    Google Scholar 

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

    Article  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 

  • D’souza B, Simpson TW (2002) A genetic algorithm based method for product family design optimization (2003). Eng Optim 35(1):1–18

    Google Scholar 

  • de Jonge B, Klingenberg W, Teunter R, Tinga T (2016) Reducing costs by clustering maintenance activities for multiple critical units. Reliab Eng Syst Saf 145:93–103

    Article  Google Scholar 

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

    MATH  Google Scholar 

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

    Article  Google Scholar 

  • Falkenauer E (1998) Genetic algorithms for grouping problems. Wiley, New York

    MATH  Google Scholar 

  • Filho EVG, Tiberti AJ (2006a) A group genetic algorithm for the machine cell formation problem. Int J Prod Econ 102:1–21

    Article  Google Scholar 

  • Filho EVG, Tiberti AJ (2006b) A group genetic algorithm for the machine cell formation problem. Int J Prod Econ 102:1–21

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Goldberg DE (1989) Genetic Algorithms: Search, Optimization and Machine Learning, Reading, MA: Addison Wesley

    Google Scholar 

  • Henn S, Koch S, Wäscher G (2012) Order batching in order picking warehouses: a survey of solution approaches. In: Manzini R (ed) Warehousing in the global supply chain: Advanced models, tools and applications for storage systems. Springer, London, pp 105–137

    Chapter  Google Scholar 

  • Ho GTS, Ip WH, Lee CKM, Moua WL (2012) Customer grouping for better resources allocation using GA based clustering technique. Expert Syst Appl 39:1979–1987

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Holland John H (1975) Adaptation in natural and artifical systems. University of Michigan Press, Ann Arbor, MI

    Google Scholar 

  • James T, Vroblefski M, Nottingham Q (2007a) A hybrid grouping genetic algorithm for the registration area planning problem. Comput Commun 30(10):2180–2190

    Article  Google Scholar 

  • James TL, Brown EC, Keeling KB (2007b) A hybrid grouping genetic algorithm for the cell formation problem. Comput Oper Res 34:2059–2079

    Article  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 

  • Kaaouache MA, Bouamama S (2015) Solving bin packing problem with a hybrid genetic algorithm for VM placement in cloud. Proc Comput Sci 60:1061–1069

    Article  Google Scholar 

  • Kashan AH, Akbari AA, Ostadi B (2015) Grouping evolution strategies: an effective approach for grouping problems. Appl Math Model 39(9):2703–2720

    Article  MathSciNet  Google Scholar 

  • Li F, Ma L, Sun Yong, Mathew J (2013) Group maintenance scheduling: a case study for a pipeline network. In Engineering asset management 2011: proceedings of the sixth annual world congress on engineering asset management (Lecture notes in mechanical engineering), Springer, Duke Energy Center, Cincinnati, Ohio, pp. 163–177

    Google Scholar 

  • Lianga Y, Leung K-S (2011) Genetic Algorithm with adaptive elitist-population strategies for multimodal function optimization. Appl Soft Comput 11:2017–2034

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Mengshoel OJ, Goldberg DE (1999) Probability crowding: deterministic crowding with probabilistic replacement. In: Banzhaf W (ed) Proceedings of the international conference. GECCO-1999, Orlando, FL, 1999, pp. 409–416

    Google Scholar 

  • Mutingi M, Mbohwa C (2013) Home healthcare worker scheduling: a group genetic algorithm approach. Proceedings of the world congress on engineering 2013, UK, 3–5 July, 2013, London, UK, pp. 721–725

    Google Scholar 

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

    Google Scholar 

  • Rakesh P, Badoni DK, Gupta Pallavi Mishra (2014) A new hybrid algorithm for university course timetabling problem using events based on groupings of students. Comput Ind Eng 78:12–25

    Article  Google Scholar 

  • Rekiek B, De Lit P, Pellichero F, Falkenauer E, Delchambre A (1999) Applying the equal piles problem to balance assembly lines. Proceedings of the 1999 IEEE international symposium on assembly and task planning Porto, Portugal, July 1999

    Google Scholar 

  • Singh A, Sevaux M, Rossi A (2009) A hybrid grouping genetic algorithm for multiprocessor scheduling. In Contemporary computing 40, communications in computer and information science. Springer Verlag Berlin Heidelberg, pp. 1–7

    Google Scholar 

  • Strnad D, Guid N (2010) A fuzzy-genetic decision support system for project team formation. Appl Soft Computing: 1178–1187

    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):9121–9134

    Article  Google Scholar 

Download references

Author information

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Charles Mbohwa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michael Mutingi .

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Mutingi, M., Mbohwa, C. (2017). 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_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-44394-2_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44393-5

  • Online ISBN: 978-3-319-44394-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation