Skip to main content
SpringerLink
Log in

Variants of Genetic Algorithms and Their Applications

  • Chapter
  • First Online:
Applied Genetic Algorithm and Its Variants

Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

Abstract

The objective of this chapter is to present a clear understanding of Genetic Algorithms and their application in advancing intelligent systems. It offers insight into the methodology of genetic algorithms and how it is employed to solve numerous engineering problems. It establishes a connection to eliminate the gap between textbooks outlining genetic algorithm methodology and more traditional books focused on genetic algorithm research. Genetic algorithm (GA), which is a subclass of the larger class of evolutionary algorithms (EA) in computer science and operations research, is a metaheuristic that takes its cues from the process of natural selection. Genetic algorithms frequently employ biologically influenced operators such as selection, mutation, and crossover to generate high-quality solutions to solve optimization and search problems. Hence, these algorithms can successfully generate solutions to extremely complex problems and it’s important to analyze the behavior of such problems to understand the importance of Genetic Algorithms. Several real-world uses for genetic algorithms in industries include manufacturing, engineering design, financial marketing, wireless sensor networks, and medical imaging. Such complex algorithms attract keen interest from people belonging to fields such as engineers, mathematicians, and computer scientists as well as niche fields such as biomedical engineering. Understanding Genetic Algorithms will provide insights into the working of algorithms in general along with explaining how solutions are obtained by these algorithms.

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 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
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

  1. Engelbrecht AP (2007) Computational intelligence: an introduction. Wiley

    Google Scholar 

  2. Gupta T (2014) Variant of genetic algorithm and its applications. Int J Artif Intell Neural Netw 4:8–12

    Google Scholar 

  3. Elsayed SM, Sarker RA, Essam DL (2010) A comparative study of different variants of genetic algorithms for constrained optimization, pp 177–186. https://doi.org/10.1007/978-3-642-17298-4_18

  4. Bhoskar T, Kulkarni O, Kulkarni N, Patekar MS, Kakandikar G, Nandedkar V (2015) Genetic algorithm and its applications to mechanical engineering: a review. Mater Today: Proc 2:2624–2630. https://doi.org/10.1016/j.matpr.2015.07.219

  5. Kakandikar G, Nandedkar V (2012) Some studies on forming optimization with genetic algorithms. Int J Optimiz Control: Theor Appl (IJOCTA) 2. https://doi.org/10.11121/ijocta.01.2012.0047

  6. Gharsalli L (2022) Hybrid genetic algorithms. https://doi.org/10.5772/intechopen.104735.

  7. Hu S, Liu H, Wu X, Li R, Zhou J, Wang J (2019) A hybrid framework combining genetic algorithm with iterated local search for the dominating tree problem. Mathematics 7:359. https://doi.org/10.3390/math7040359

    Article  Google Scholar 

  8. Cheng JR, Gen M (2020) Parallel genetic algorithms with GPU computing. In: Impact on intelligent logistics and manufacturing

    Google Scholar 

  9. Cheng H, Yang S (2010) Multi-population genetic algorithms with immigrants scheme for dynamic shortest path routing problems in mobile ad hoc networks. In: Applications of evolutionary computation. Springer, pp 562–571

    Google Scholar 

  10. Cheng H, Yang S, Cao J (2013) Dynamic genetic algorithms for the dynamic load balanced clustering problem in mobile ad hoc net-works. Expert Syst Appl 40(4):1381–1392

    Article  Google Scholar 

  11. Chouhan SS, Kaul A, Singh UP (2018) Soft computing approaches for image segmentation: a survey. Multimed Tools Appl 77(21):28483–28537

    Article  Google Scholar 

  12. Baker JE, Grefenstette J (2014) Proceedings of the first international conference on genetic algorithms and their applications. Taylor and Francis, Hoboken, pp 101–105

    Google Scholar 

  13. Bolboca SD, JAntschi L, Balan MC, Diudea MV, Sestras RE (2010) State of art in genetic algorithms for agricultural systems. Not Bot Hort Agrobot Cluj 38(3):51–63

    Google Scholar 

  14. Bonabeau E, Dorigo M, Theraulaz G (1999) Swarm intelligence: from natural to artificial systems. Oxford University Press, Inc.

    Google Scholar 

  15. Burchardt H, Salomon R (2006) Implementation of path planning using genetic algorithms on mobile robots. In: IEEE international conference on evolutionary computation, Vancouver, BC, pp 1831–1836

    Google Scholar 

  16. Burkowski FJ (1999) Shuffle crossover and mutual information. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), Washington, DC, USA, pp 1574–1580

    Google Scholar 

  17. Chaiyaratana N, Zalzala AM (2000) Hybridisation of neural networks and a genetic algorithm for friction compensation. The 2000 congress on evolutionary computation, vol 1. San Diego, USA, pp 22–29

    Google Scholar 

  18. Chen R, Liang C-Y, Hong W-C, Gu D-X (2015) Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. Appl Soft Comput 26:434–443

    Article  Google Scholar 

  19. Snaselova P, Zboril F (2015) Genetic algorithm using theory of chaos. Procedia Comput Sci 51:316–325

    Article  Google Scholar 

  20. Hao K, Zhao J, Wang B, Liu Y, Wang C (2021) The application of an adaptive genetic algorithm based on collision detection in path planning of mobile robots. Comput Intell Neurosci 2021:20, Article ID 5536574. https://doi.org/10.1155/2021/5536574

  21. Ye F, Qi W, Xiao J (2011) Research of niching genetic algorithms for optimization in electromagnetics. Procedia Eng 16:383–389. https://doi.org/10.1016/j.proeng.2011.08.1099

    Article  Google Scholar 

  22. Dou R, Zong C, Li M (2016) An interactive genetic algorithm with the interval arithmetic based on hesitation and its application to achieve customer collaborative product configuration design. Appl Soft Comput 38:384–394

    Article  Google Scholar 

  23. Bhasin H, Bhatia S (2012) Application of genetic algorithms in machine learning

    Google Scholar 

  24. Langdon W, Poli R (2002) Foundations of genetic programming. https://doi.org/10.1007/978-3-662-04726-2

  25. Bhasin H, Arora N (2011) Cryptography using genetic algorithms

    Google Scholar 

  26. Li C (2020) Optimization of human resources allocation for airport security check business based on genetic algorithm, 983–986. https://doi.org/10.1109/ICEMME51517.2020.00201

  27. Jennane R, Almhdie-Imjabber A, Hambli R, Ucan ON, Benhamou CL (2010) Genetic algorithm and image processing for osteoporosis diagnosis. In: Conference proceedings: ... Annual international conference of the IEEE engineering in medicine and biology society, pp 5597–600. https://doi.org/10.1109/IEMBS.2010.5626804

  28. Kadhim M (2018) Medical image processing using the hybrid genetic algorithm. J Eng Appl Sci 13:7248–7252. https://doi.org/10.3923/jeasci.2018.7248.7252

    Article  Google Scholar 

  29. Arakaki RK, Usberti FL (2018) Hybrid genetic algorithm for the open capacitated arc routing problem. Comput Oper Res 90:221–231

    Article  MathSciNet  MATH  Google Scholar 

  30. Arkhipov DI, Wu D, Wu T, Regan AC (2020) A parallel genetic algorithm framework for transportation planning and logistics management. IEEE Access 8:106506–106515

    Article  Google Scholar 

  31. Azadeh A, Elahi S, Farahani MH, Nasirian B (2017) A genetic algorithm-Taguchi based approach to inventory routing problem of a single perishable product with transshipment. Comput Ind Eng 104:124–133

    Article  Google Scholar 

  32. Chuang YC, Chen CT, Hwang C (2016) A simple and efficient real-coded genetic algorithm for constrained optimization. Appl Soft Comput 38:87–105

    Article  Google Scholar 

  33. Coello CAC, Pulido GT (2001) A micro-genetic algorithm for multiobjective optimization. In: EMO, volume 1993 of lecture notes in computer science. Springer, pp 126–140

    Google Scholar 

  34. Das KN (2014) Hybrid genetic algorithm: an optimization tool. In: Global trends in intelligent computing research and development. IGI Global, pp 268–305

    Google Scholar 

  35. Karaa WBA, Ashour AS, Sassi DB, Roy P, Kausar N, Dey N (2016) Medline text mining: an enhancement genetic algorithm based approach for document clustering. In: Applications of intelligent optimization in biology and medicine: current trends and open problems, pp 267–287

    Google Scholar 

  36. Das AK, Pratihar DK (2018) A direction-based exponential mutation operator for real-coded genetic algorithm. In: IEEE international conference on emerging applications of information technology

    Google Scholar 

  37. Dash SR, Dehuri S, Rayaguru S (2013) Discovering interesting rules from biological data using parallel genetic algorithm. In: 3rd IEEE international advance computing conference (IACC), Ghaziabad, pp 631–636

    Google Scholar 

  38. Datta D, Amaral ARS, Figueira JR (2011) Single row facility layout problem using a permutation-based genetic algorithm. Euro J Oper Res 213(2):388–394

    Article  MathSciNet  MATH  Google Scholar 

  39. Chatterjee S, Sarkar S, Hore S, Dey N, Ashour AS, Shi F, Le DN (2017) Structural failure classification for reinforced concrete buildings using trained neural network based multi-objective genetic algorithm. Struct Eng Mech 63(4):429–438

    Google Scholar 

  40. de Ocampo ALP, Dadios EP (2017) Energy cost optimization in irrigation system of smart farm by using genetic algorithm. In: 2017 IEEE 9th international conference on humanoid. Nanotechnology, information technology, communication and control, environment and management (HNICEM), Manila, pp 1–7

    Google Scholar 

  41. Chatterjee S, Sarkar S, Dey N, Sen S, Goto T, Debnath NC (2017) Water quality prediction: multi objective genetic algorithm coupled artificial neural network based approach. In: 2017 IEEE 15th international conference on industrial informatics (INDIN). IEEE, pp 963–968

    Google Scholar 

  42. Chatterjee S, Sarkar S, Dey N, Ashour AS, Sen S (2018) Hybrid non-dominated sorting genetic algorithm: II-neural network approach. In: Advancements in applied metaheuristic computing. IGI Global, pp 264–286

    Google Scholar 

  43. Gupta N, Khosravy M, Gupta S, Dey N, Crespo RG (2022) Lightweight artificial intelligence technology for health diagnosis of agriculture vehicles: parallel evolving artificial neural networks by genetic algorithm. Int J Parallel Program 1–26

    Google Scholar 

  44. Dey N, Ashour AS, Beagum S, Sifaki Pistola D, Gospodinov M, Peneva Gospodinova E, Manuel RS, Tavares J (2015) Parameter optimization for local polynomial approximation based intersection confidence interval filter using genetic algorithm: an application for brain MRI image de-noising. J Imaging 1(1):60–84

    Article  Google Scholar 

  45. Ashour AS, Nagieb RM, El-Khobby HA, Abd Elnaby MM, Dey N (2021) Genetic algorithm-based initial contour optimization for skin lesion border detection. Multimed Tools Appl 80:2583–2597

    Article  Google Scholar 

  46. Deb K, Agrawal RB (1995) Simulated binary crossover for continuous search space. Complex Syst 9:115–148

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of ECE, VIT, Vellore, India

    Radha Debal Goswami

  2. Department of CSE, Techno International New Town, Kolkata, India

    Sayan Chakraborty

  3. Department of CSE, Techno International New Town, Kolkata, India

    Bitan Misra

Authors
  1. Radha Debal Goswami
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sayan Chakraborty
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bitan Misra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bitan Misra .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Techno International New Town, Kolkata, West Bengal, India

    Nilanjan Dey

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Goswami, R.D., Chakraborty, S., Misra, B. (2023). Variants of Genetic Algorithms and Their Applications. In: Dey, N. (eds) Applied Genetic Algorithm and Its Variants. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-99-3428-7_1

Download citation

Publish with us

Policies and ethics

Search

Navigation