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A Study of Crossover Operators in Genetic Algorithms

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Frontiers in Nature-Inspired Industrial Optimization

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Abstract

Crossover is an important operator in genetic algorithms. Although hundreds of application dependent and independent crossover operators exist in the literature, this chapter provides holistic, but by no means an exhaustive, overview of different crossover techniques used in different variants of genetic algorithms. We will review some of the commonly used crossover operators in binary-coded genetic algorithms as well as in real-coded genetic algorithms and explore the use cases and performance of different techniques for different applications to provide a better understanding of the types of bias exhibited by different crossover operators. This knowledge can be useful when designing an algorithm for a specific problem, particularly if there are known patterns or dependencies in the selected representation.

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References

  1. Dey N (2017) Advancements in applied metaheuristic computing. IGI Global

    Google Scholar 

  2. Khosravy M, Gupta N, Patel N, Senjyu T (2020) Frontier applications of nature inspired computation. Springer

    Google Scholar 

  3. Khosravy M, Gupta N, Patel N, Senjyu T, Duque CA (2020) Particle swarm optimization of morphological filters for electrocardiogram baseline drift estimation. In: Applied nature-inspired computing: algorithms and case studies. Springer, pp 1–21

    Google Scholar 

  4. Chawda GS, Shaik AG, Shaik M, Padmanaban S, Holm-Nielsen JB, Mahela OP, Kaliannan P (2020) Comprehensive review on detection and classification of power quality disturbances in utility grid with renewable energy penetration. IEEE Access, vol 8, pp 146 807–146 830

    Google Scholar 

  5. Gupta N, Khosravy M, Patel N, Dey N, Gupta S, Darbari H, Crespo RG (2020) Economic data analytic AI technique on IoT edge devices for health monitoring of agriculture machines. Appl Intell 50(11):3990–4016

    Article  Google Scholar 

  6. Khosravy M, Gupta N, Patel N, Dey N, Nitta N, Babaguchi N (2020) Probabilistic stone’s blind source separation with application to channel estimation and multi-node identification in mimo IoT green communication and multimedia systems. Comput Commun 157:423–433

    Article  Google Scholar 

  7. Gupta N, Gupta S, Khosravy M, Dey N, Joshi N, Crespo RG, Patel N (2020) Economic IoT strategy: the future technology for health monitoring and diagnostic of agriculture vehicles. J Intell Manuf 1–12

    Google Scholar 

  8. Khosravy M, Gupta N, Dey N, Ger PM (2021) Smart green ocean underwater IoT network by ICA-based acoustic blind mimo of DM transceiver with analysis of acoustic channel sparsity and blind estimation efficinecy in data rate and energy consumption. Earth Sci Inf

    Google Scholar 

  9. Deb K (2012) Optimization for engineering design: algorithms and examples. PHI Learning Pvt Ltd

    Google Scholar 

  10. Razali NM, Geraghty J et al (2011) Genetic algorithm performance with different selection strategies in solving tsp. In: Proceedings of the world congress on engineering. International Association of Engineers Hong Kong, vol 2, pp 1–6

    Google Scholar 

  11. Beasley D, Bull DR, Martin RR (1993) An overview of genetic algorithms: Part 1, fundamentals. Univ Comput 15(2):56–69

    Google Scholar 

  12. Gupta N, Khosravy M, Patel N, Dey N, Mahela OP (2020) Mendelian evolutionary theory optimization algorithm. Soft Comput 24(19), 14 345–14 390

    Google Scholar 

  13. Gupta N, Khosravy M, Patel N, Sethi I (2018) Evolutionary optimization based on biological evolution in plants. Procedia Comput Sci 126:146–155

    Article  Google Scholar 

  14. Gupta N, Khosravy M, Mahela OP, Patel N (2020) Plant biologyinspired genetic algorithm: Superior efficiency to firefly optimizer. In: Applications of firefly algorithm and its variants. Springer

    Google Scholar 

  15. VarunKumar S, Panneerselvam R (2017) A study of crossover operators for genetic algorithms to solve VRP and its variants and new sinusoidal motion crossover operator. Int J Comput Intell Res 13(7):1717–1733

    Google Scholar 

  16. Umbarkar AJ, Sheth PD (2015) Crossover operators in genetic algorithms: a review. ICTACT J Soft Comput 6(1)

    Google Scholar 

  17. Gupta N, Patel N, Tiwari BN, Khosravy M (2018) Genetic algorithm based on enhanced selection and log-scaled mutation technique. In: Proceedings of the future technologies conference. Springer, pp 730–748

    Google Scholar 

  18. Collard P, Escazut C (1995) Genetic operators in a dual genetic algorithm. In: Proceedings of 7th IEEE international conference on tools with artificial intelligence. IEEE, pp 12–19

    Google Scholar 

  19. Eiben ÁE, Hinterding R, Michalewicz Z (1999) Parameter control in evolutionary algorithms. IEEE Trans Evol Comput 3(2):124–141

    Article  Google Scholar 

  20. Tiwari BN, Kibinde JK, Gupta N, Khosravy M, Bellucci S (2021) Optimization of optical instruments under fluctuations of system parameters. Int J Ambient Comput Intell (ACI) 12(1):73–113

    Google Scholar 

  21. Foth M, Schroeter R, Ti J (2013) Opportunities of public transport experience enhancements with mobile services and urban screens. Int J Ambient Comput Intell (ACI) 5(1):1–18

    Google Scholar 

  22. Melo K, Khosravy M, Duque C, Dey N (2020) Chirp code deterministic compressive sensing: analysis on power signal. In: 4th international conference on information technology and intelligent transportation systems. IOS Press, pp 125–134

    Google Scholar 

  23. Santos E, Khosravy M, Lima MA, Cerqueira AS, Duque CA, Yona A (2019) High accuracy power quality evaluation under a colored noisy condition by filter bank esprit. Electronics 8(11):1259

    Article  Google Scholar 

  24. Santos E, Khosravy M, Lima MA, Cerqueira AS, Duque CA (2020) Esprit associated with filter bank for power-line harmonics, sub-harmonics and inter-harmonics parameters estimation. Int J Electr Power Energy Syst 118:105 731

    Google Scholar 

  25. Baumgarten M, Mulvenna MD, Rooney N, Reid J (2013) Keyword based sentiment mining using twitter. Int J Ambient Comput Intell (ACI) 5(2):56–69

    Google Scholar 

  26. Gutierrez CE, Alsharif PMR, Khosravy M, Yamashita PK, Miyagi PH, Villa R (2014) “Main large data set features detection by a linear predictor model,” in AIP conference proceedings. Am Inst Phys 1618:733–737

    Google Scholar 

  27. Yamin M, Abi Sen AA (2018) Improving privacy and security of user data in location based services. Int J Ambient Comput Intell (ACI) 9(1), 19–42

    Google Scholar 

  28. Picorone AA, de Oliveira TR, Sampaio-Neto R, Khosravy M, Ribeiro MV (2020) Channel characterization of lowvoltage electric power distribution networks for plc applications based on measurement campaign. Int J Electr Power Energy Syst 116:105–554

    Google Scholar 

  29. Khosravy M, Gupta N, Marina N, Sethi IK, Asharif MR (2017) Perceptual adaptation of image based on chevreul-mach bands visual phenomenon. IEEE Signal Process Lett 24(5):594–598

    Article  Google Scholar 

  30. Khosravy M, Gupta N, Marina N, Sethi IK, Asharif MR (2017) Brain action inspired morphological image enhancement. In: Nature-inspired computing and optimization. Springer, pp 381–407

    Google Scholar 

  31. Khosravy M, Nitta N, Asharif F, Melo K, Duque CA (2020) Deterministic compressive sensing by chirp codes: a matlab® tutorial. In: Compressive sensing in healthcare. Elsevier, pp 125–144

    Google Scholar 

  32. Ramalho D, Melo K, Khosravy M, Asharif F, Danish MSS, Duque CA (2020) A review of deterministic sensing matrices. Compressive Sens Healthc, pp 89–110

    Google Scholar 

  33. Cabral TW, Khosravy M, Dias FM, Monteiro HLM, Lima MAA, Silva LRM, Naji R, Duque CA (2019) Compressive sensing in medical signal processing and imaging systems. In: Sensors for health monitoring. Elsevier, pp 69–92

    Google Scholar 

  34. Dias FM, Khosravy M, Cabral TW, Monteiro HLM, de Andrade Filho LM, de Mello Honório L, Naji R, Duque CA (2020) Compressive sensing of electrocardiogram. In: Compressive sensing in healthcare. Elsevier, pp 165–184

    Google Scholar 

  35. Khosravy M, Gupta N, Patel N, Duque CA, Nitta N, Babaguchi N (2020) Deterministic compressive sensing by chirp codes: a descriptive tutorial. In: Compressive sensing in healthcare. Elsevier, pp 111–124

    Google Scholar 

  36. Resende DF, Khosravy M, Monteiro HL, Gupta N, Patel N, Duque CA (2020) Neural signal compressive sensing. Compressive sensing in healthcare, pp 201–221

    Google Scholar 

  37. de Oliveira MM, Khosravy M, Monteiro HL, Cabral TW, Dias FM, Lima MA, Silva LRM, Duque CA (2020) Compressive sensing of electroencephalogram: a review. Compressive sensing in healthcare, pp 247–268

    Google Scholar 

  38. Khosravy M, Gupta N, Patel N, Duque CA (2020) Recovery in compressive sensing: a review. Compressive sensing in healthcare, pp 25–42

    Google Scholar 

  39. Khosravy M, Nitta N, Nakamura K, Babaguchi N (2020) Compressive sensing theoretical foundations in a nutshell. In: Compressive sensing in healthcare. Elsevier, pp 1–24

    Google Scholar 

  40. Gupta S, Khosravy M, Gupta N, Darbari H, Patel N (2019) Hydraulic system onboard monitoring and fault diagnostic in agricultural machine. Brazilian Archives of Biology and Technology, vol 62

    Google Scholar 

  41. Gupta S, Khosravy M, Gupta N, Darbari H (2019) In-field failure assessment of tractor hydraulic system operation via pseudospectrum of acoustic measurements. Turkish J Electr Eng Comput Sci 27(4):2718–2729

    Google Scholar 

  42. Gupta N, Kini P, Gupta S, Darbari H, Joshi N, Khosravy M (2021) Six sigma based modeling of the hydraulic oil heating under low load operation. Eng Sci Technol Int J 24(1):11–21

    Google Scholar 

  43. Kale GV, Patil VH (2016) A study of vision based human motion recognition and analysis. Int J Ambient Comput Intell (ACI) 7(2):75–92

    Google Scholar 

  44. Gutierrez CE, Alsharif MR, Yamashita K, Khosravy M (2014) A tweets mining approach to detection of critical events characteristics using random forest. Int J Next-Gener Comput 5(2):167–176

    Google Scholar 

  45. Kausar N, Palaniappan S, Samir BB, Abdullah A, Dey N (2016) Systematic analysis of applied data mining based optimization algorithms in clinical attribute extraction and classification for diagnosis of cardiac patients. In: Applications of intelligent optimization in biology and medicine. Springer, pp 217–231

    Google Scholar 

  46. Gutierrez CE, Alsharif MR, Cuiwei H, Khosravy M, Villa R, Yamashita K, Miyagi H (2013) Uncover news dynamic by principal component analysis. ICIC Express Lett 7(4):1245–1250

    Google Scholar 

  47. Gupta N, Khosravy M, Patel N, Senjyu T (2018) A bi-level evolutionary optimization for coordinated transmission expansion planning. IEEE Access, vol 6, pp 48 455–48 477

    Google Scholar 

  48. Gupta N, Khosravy M, Saurav K, Sethi IK, Marina N (2018) Value assessment method for expansion planning of generators and transmission networks: a non-iterative approach. Electr Eng 100(3):1405–1420

    Article  Google Scholar 

  49. Hemalatha S, Anouncia SM (2017) Unsupervised segmentation of remote sensing images using fd based texture analysis model and isodata. Int J Ambient Comput Intell (ACI) 8(3):58–75

    Google Scholar 

  50. Khosravy M (2009) A blind ICA based receiver with efficient multiuser detection for multi-input multi-output ofdm systems. In: The 8th international conference on applications and principles of information science (APIS), Okinawa, Japan, pp 311–314

    Google Scholar 

  51. Khosravy M, Punkoska N, Asharif F, Asharif MR (2014) “Acoustic ofdm data embedding by reversible walsh-hadamard transform,” in AIP conference proceedings. Am Inst Phys 1618:720–723

    Google Scholar 

  52. Khosravy M, Alsharif MR, Guo B, Lin H, Yamashita K (2009) A robust and precise solution to permutation indeterminacy and complex scaling ambiguity in bss-based blind mimo-ofdm receiver. In: International conference on independent component analysis and signal separation. Springer, pp 670–677

    Google Scholar 

  53. Khosravy M, Alsharif MR, Yamashita K (2009) An efficient ICA based approach to multiuser detection in mimo OFDM systems. Multi-carrier Syst Solu 2009:47–56

    Google Scholar 

  54. Khosravy M, Alsharif MR, Khosravi M, Yamashita K (2010) An optimum pre-filter for ica based mulit-input multi-output FDM system. In: 2nd international conference on education technology and computer, vol 5. IEEE, pp V5–129

    Google Scholar 

  55. Khosravy M, Kakazu S, Alsharif MR, Yamashita K (2010) Multiuser data separation for short message service using ICA. SIP: IEICE Tech Rep 109(435):113–117

    Google Scholar 

  56. Alenljung B, Lindblom J, Andreasson R, Ziemke T (2019) User experience in social human-robot interaction. In: Rapid automation: concepts, methodologies, tools, and applications. IGI Glob 1468–1490

    Google Scholar 

  57. Khosravy M, Asharif MR, Sedaaghi MH (2008) Medical image noise suppression: using mediated morphology. MI 107(461):265–270

    Google Scholar 

  58. Dey N, Ashour AS, Ashour AS, Singh A (2015) Digital analysis of microscopic images in medicine. J Adv Microsc Res 10(1):1–13

    Article  Google Scholar 

  59. Castelfranchi C, Pezzulo G, Tummolini L (2010) Behavioral implicit communication (bic): Communicating with smart environments. Int J Ambient Comput Intell (ACI) 2(1):1–12

    Google Scholar 

  60. Khosravy M, Asharif MR, Sedaaghi MH (2008) Morphological adult and fetal ECG preprocessing: employing mediated morphology. MI 107(461):363–369

    Google Scholar 

  61. Sedaaghi MH, Daj R, Khosravi M (2001) Mediated morphological filters. In: Proceedings 2001 international conference on image processing (Cat No 01CH37205), vol 3. IEEE, pp 692–695

    Google Scholar 

  62. Dey N, Mukhopadhyay S, Das A, Chaudhuri SS (2012) Analysis of p-qrs-t components modified by blind watermarking technique within the electrocardiogram signal for authentication in wireless telecardiology using dwt. Int J Image, Graph Signal Process 4(7)

    Google Scholar 

  63. Dey N, Samanta S, Yang X-S, Das A, Chaudhuri SS (2013) Optimisation of scaling factors in electrocardiogram signal watermarking using cuckoo search. Int J Bio-Inspired Comput 5(5):315–326

    Article  Google Scholar 

  64. Dey N, Ashour AS, Shi F, Fong SJ, Sherratt RS (2017) Develo** residential wireless sensor networks for ECG healthcare monitoring. IEEE Trans Consum Electron 63(4):442–449

    Article  Google Scholar 

  65. Holland JH et al (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press

    Google Scholar 

  66. Pavai G, Geetha T (2016) A survey on crossover operators. ACM Comput Surv (CSUR) 49(4):1–43

    Article  Google Scholar 

  67. Eshelman LJ, Caruana RA, Schaffer JD (1989) Biases in the crossover landscape. In: Proceedings of the third international conference on Genetic algorithms, pp 10–19

    Google Scholar 

  68. Rana S (1999) The distributional biases of crossover operators. In: Proceedings of the genetic and evolutionary computation conference, Citeseer, pp 549–556

    Google Scholar 

  69. Syswerda G (1993) Simulated crossover in genetic algorithms. In: Foundations of genetic algorithms, vol 2. Elsevier, pp 239–255

    Google Scholar 

  70. Zbigniew M (1996) Genetic algorithms + data structures= evolution programs. Comput Stat 372–373

    Google Scholar 

  71. Eiben AE, Smith JE et al (2003) Introduction to evolutionary computing, vol 53. Springer

    Google Scholar 

  72. Mitchell M (1998) An introduction to genetic algorithms. MIT Press

    Google Scholar 

  73. Singh G, Gupta N, Khosravy M (2015) New crossover operators for real coded genetic algorithm (RCGA). In: 2015 international conference on intelligent informatics and biomedical sciences (ICIIBMS). IEEE pp 135–140

    Google Scholar 

  74. Picek S, Jakobovic D, Golub M (2013) On the recombination operator in the real-coded genetic algorithms. In: IEEE congress on evolutionary computation. IEEE, pp 3103–3110

    Google Scholar 

  75. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  76. Eshelman LJ, Schaffer JD (1993) Real-coded genetic algorithms and interval-schemata,” in Foundations of genetic algorithms, vol 2. Elsevier, pp 187–202

    Google Scholar 

  77. Deb K, Agrawal RB et al (1995) Simulated binary crossover for continuous search space. Compl Syst 9(2):115–148

    MathSciNet  MATH  Google Scholar 

  78. Goldberg DE, Lingle R et al (1985) Alleles, loci, and the traveling salesman problem. In: Proceedings of an international conference on genetic algorithms and their applications, vol 154. Lawrence Erlbaum Hillsdale, NJ, pp 154–159

    Google Scholar 

  79. Ting C-K (2004) An analysis of the effectiveness of multi-parent crossover. In: International conference on parallel problem solving from nature. Springer, pp 131–140

    Google Scholar 

  80. Goldberg DE (1989) Genetic algorithms in search. Optim Mach Learn

    Google Scholar 

  81. Altenberg L (1995) The schema theorem and price’s theorem. In: Foundations of genetic algorithms, vol 3. Elsevier, pp 23–49

    Google Scholar 

  82. Syswerda G (1989) Uniform crossover in genetic algorithms. In: Proceedings of the 3rd international conference on genetic algorithms, pp 2–9

    Google Scholar 

  83. Spears WM, De Jong KD (1995) On the virtues of parameterized uniform crossover. Technical report. Naval Research Lab, Washington DC

    Book  Google Scholar 

  84. Grefenstette JJ (1986) Optimization of control parameters for genetic algorithms. IEEE Trans Syst Man Cybern 16(1):122–128

    Article  Google Scholar 

  85. Rowe JE, Vose MD, Wright AH (2002) Group properties of crossover and mutation. Evol Comput 10(2):151–184

    Article  Google Scholar 

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Authors and Affiliations

  1. Indian Institute of Technology Jodhpur, Karwar, India

    Gurjot Singh

  2. Oakland University, Rochester, MI, USA

    Neeraj Gupta

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  1. Gurjot Singh
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Correspondence to Neeraj Gupta .

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Editors and Affiliations

  1. Cross labs, Cross-Compass Ltd, Osaka, Tokyo, Japan

    Mahdi Khosravy

  2. Computer Science and Engineering, Oakland University, Rochester, MI, USA

    Neeraj Gupta

  3. Computer Science and Engineering, Oakland University, Rochester, MI, USA

    Nilesh Patel

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Singh, G., Gupta, N. (2022). A Study of Crossover Operators in Genetic Algorithms. In: Khosravy, M., Gupta, N., Patel, N. (eds) Frontiers in Nature-Inspired Industrial Optimization. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-16-3128-3_2

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