Communications - Scientific Letters of the University of Zilina 2025, 27(3):E35-E45 | DOI: 10.26552/com.C.2025.036

The Influence of Three Parent Crossbreeding on the Dual Population Genetic Algortihm

Esra'a Alkafaween ORCID...1, 2, *, Obada Alhabashneh2, Maram M. Al-Mjali2
1 Department of Computer Science, Alzaytoonah University of Jordan, Amman, Jordan
2 Data Science and Artificial Intelligence Department, Mutah University, Karak, Jordan

A genetic algorithm (GA) is an optimization technique based on natural genetics, using selection, crossover, and mutation. Crossover combines genetic material from two parents to create offspring, maintaining diversity and preventing premature convergence. While the two parents are typically used, multi-parent crossover, involving more than two parents, has shown superior results. in this paper is explored the multi-parent crossover in dual genetic algorithms, which facilitate information exchange between populations through interpolation crossbreeding. Offspring inherit traits from both parent populations, improving adaptability. The Cave-Surface GA (CSGA) with three-parent crossover is tested on 15 Travelling Salesman Problem (TSP) benchmarks. Results show that the CSGA outperforms both traditional GAs and two-parent CSGA. This method demonstrates great potential for complex optimization challenges.

Keywords: genetic algorithm, TSP, multipopulation, multi-parent order crossover, cave surface GA
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The authors received no financial support for the research, authorship and/or publication of this article.

Conflicts of interest:

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Received: September 23, 2024; Accepted: March 17, 2025; Prepublished online: April 16, 2025; Published: July 1, 2025  Show citation

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Alkafaween, E., Alhabashneh, O., & Al-Mjali, M.M. (2025). The Influence of Three Parent Crossbreeding on the Dual Population Genetic Algortihm. Communications - Scientific Letters of the University of Zilina, 27(3), E35-45. doi: 10.26552/com.C.2025.036
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    References

    1. LIU, X., DENG, Y. A new QoS-aware service discovery technique in the internet of things using whale optimization and genetic algorithms. Journal of Engineering and Applied Science. 2024, 71(1), 4. ISSN 1110-1903, eISSN 2536-9512. Available from: https://doi.org/10.1186/s44147-023-00334-1‏ Go to original source...
    2. ALKAFAWEEN, E., HASSANAT, A. B., TARAWNEH, S. Improving initial population for genetic algorithm using the multi linear regression based technique (MLRBT). Communications - Scientific Letters of the University of Zilina [online]. 2021, 23(1), p. E1-E10. ISSN 1335-4205, eISSN 2585-7878. Available from: https://doi.org/10.26552/com.C.2021.1.E1-E10 Go to original source...
    3. HOLLAND, J. H. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence [online]. MIT press, 1992. ISBN 9780262275552. Available from: https://doi.org/10.7551/mitpress/1090.001.0001 Go to original source...
    4. BALA, A., SHARMA, A. K. A comparative study of modified crossover operators. In: 2015 3rd International Conference on Image Information Processing ICIIP: proceedings [online]. IEEE. 2015. ISBN 978-1-5090-0148-4. Available from: https://doi.org/10.1109/ICIIP.2015.7414781 Go to original source...
    5. ALKAFAWEEN, E. O. Novel methods for enhancing the performance of genetic algorithms [online]. Master's thesis. Jordan: Mutah University, 2015. Available from: https://doi.org/10.48550/arXiv.1801.02827 Go to original source...
    6. ALKAFAWEEN, E., HASSANAT, A. B. Improving TSP solutions using GA with a new hybrid mutation based on knowledge and randomness. Communications - Scientific Letters of the University of Zilina [online]. 2020, 22(3), p. 128-139. ISSN 1335-4205, eISSN 2585-7878. Available from: https://doi.org/10.26552/com.C.2020.3.128-139 Go to original source...
    7. HASSANAT, A. B., ALKAFAWEEN, E. On enhancing genetic algorithms using new crossovers. International Journal of Computer Applications in Technology [online]. 2017, 55(3), p. 202-212. ISSN 0952-8091, eISSN 1741-5047. Available from: https://doi.org/10.1504/IJCAT.2017.10005868 Go to original source...
    8. HASSANAT, A. B., ALKAFAWEEN, E., AL-NAWAISEH, N. A., ABBADI, M. A., ALKASASSBEH, M., ALHASANAT, M. B. Enhancing genetic algorithms using multi mutations: experimental results on the travelling salesman problem. International Journal of Computer Science and Information Security. 2016, 14(7), 785. eISSN 1947-5500. Go to original source...
    9. AHMED, Z. H. Multi-parent extension of sequential constructive crossover for the travelling salesman problem. International Journal of Operational Research [online]. 2011, 11(3), p. 331-342. ISSN 1745-7645, eISSN 1745-7653. Available from: https://doi.org/10.1504/IJOR.2011.041347 Go to original source...
    10. LEUNG, Y., GAO Y., XU, Z.-B. Degree of population diversity - a perspective on premature convergence in genetic algorithms and its Markov chain analysis. IEEE Transactions on Neural Networks [online]. 1997, 8(5), p. 1165-1176. ISSN 1045-9227. Available from: https://doi.org/10.1109/72.62321 Go to original source...
    11. LIU, L., FEI, T., ZHU, Z., WU, K., ZHANG, Y. A survey of evolutionary algorithms. In: 2023 4th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering ICBAIE: proceedings [online]. IEEE. 2023. eISBN 979-8-3503-4361-8, p. 22-27. Available from: https://doi.org/10.1109/ICBAIE59714.2023.10281260 Go to original source...
    12. HASSANAT, A., ALMOHAMMADI, K., ALKAFAWEEN, E., ABUNAWAS, E., HAMMOURI, A., PRASATH, V. S. Choosing mutation and crossover ratios for genetic algorithms - a review with a new dynamic approach. Information [online]. 2019, 10(12), 390. eISSN 2078-2489. Available from: https://doi.org/10.3390/info10120390 Go to original source...
    13. XUE, Y., ZHU, H., LIANG, J., SLOWIK, A. Adaptive crossover operator based multi-objective binary genetic algorithm for feature selection in classification. Knowledge - Based Systems [online]. 2021, 227, 107218. ISSN 0950-7051, eISSN 1872-7409. Available from: https://doi.org/10.1016/j.knosys.2021.107218 Go to original source...
    14. JEBARI, K., BOUROUMI, A., ETTOUHAMI, A. Parameters control in GAs for dynamic optimization. International Journal of Computational Intelligence Systems [online]. 2013, 6(1), p. 47-63. eISSN 1875-6883. Available from: https://doi.org/10.1080/18756891.2013.754172 Go to original source...
    15. WHITLEY, D., RANA, S., HECKENDORN, R. B. The Island model genetic algorithm: on separability, population size and convergence. Journal of Computing and Information Technology. 1999, 7, p. 33-48. ISSN 1330-1136, eISSN 1846-3908.
    16. DEB, K. Multi-objective evolutionary algorithms. In: Springer handbook of computational intelligence [online]. KACPRZYK, J., PEDRYCZ, W. (Eds.). Berlin Heidelberg: Springer-Verlag, 2015. p. 995-1015. ISBN 978-3-662-43504-5, eISBN 978-3-662-43505-2. Available from: https://doi.org/10.1007/978-3-662-43505-2 Go to original source...
    17. DENG, D.-S., LONG, W., LI, Y.-Y., SHI, X.-Q. Multipopulation genetic algorithms with different interaction structures to solve flexible job-shop scheduling problems: a network science perspective. Mathematical Problems in Engineering [online]. 2020, 2020(1), 8503454. ISSN 1024-123X, eISSN 1563-5147. Available from: https://doi.org/10.1155/2020/8503454 Go to original source...
    18. SHI, X., LONG, W., LI, Y., DENG, D. Multi-population genetic algorithm with ER network for solving flexible job shop scheduling problems. PloS One [online]. 2020, 15(5), e0233759. eISSN 1932-6203. Available from: https://doi.org/10.1371/journal.pone.0233759 Go to original source...
    19. PARK, T., RYU, K. R. A dual population genetic algorithm with evolving diversity. In: 2007 IEEE Congress on Evolutionary Computation: proceedings [online]. IEEE. 2007. ISSN 1089-778X, eISSN 1941-0026, ISBN 978-1-4244-1339-3. Available from: https://doi.org/10.1109/CEC.2007.4424928 Go to original source...
    20. YANG, S. PDGA: the primal - dual genetic algorithm. In: Design and application of hybrid intelligent systems [online]. ABRAHAM, A., KOPPEN, M., FRANKE, K. (Eds.). Amsterdam: IOS Press, 2003. ISBN 978-1586033941, p. 214-223. Available from: https://doi.org/10.13140/RG.2.1.3134.3445 Go to original source...
    21. ALKAFAWEEN, E., ELMOUGY, S., ESSA, E., MNASRI, S., TARAWNEH, A. S., HASSANAT, A. IAM-TSP: iterative approximate methods for solving the travelling salesman problem. International Journal of Advanced Computer Science and Applications [online]. 2023, 14(11), p. 420-428. ISSN 2158-107X, eISSN 2156-5570. Available from: https://doi.org/10.14569/IJACSA.2023.0141143 Go to original source...
    22. ALKAFAWEEN, E., HASSANAT, A., ESSA, E., ELMOUGY, S. An efficiency boost for genetic algorithms: initializing the GA with the iterative approximate method for optimizing the traveling salesman problem - experimental insights. Applied Sciences [online]. 2024, 14(8), 3151. eISSN 2076-3417. Available from: https://doi.org/10.3390/app14083151 Go to original source...
    23. YU, Y., CHEN, Y., LI, T. A new design of genetic algorithm for solving TSP. In: 2011 4th International Joint Conference on Computational Sciences and Optimization: proceedings [online]. IEEE. 2011. ISBN 978-1-4244-9712-6. Available from: https://doi.org/10.1109/CSO.2011.46 Go to original source...
    24. KALEYBAR, H. J., DAVOODI, M., BRENNA M, ZANINELLI, D. Applications of genetic algorithm and its variants in rail vehicle systems: a bibliometric analysis and comprehensive review. IEEE Access [online]. 2023, 11, p. 68972-68993. eISSN 2169-3536. Available from: https://doi.org/10.1109/ACCESS.2023.3292790 Go to original source...
    25. AMIRI, F. Optimization of facility location-allocation model for base transceiver station antenna establishment based on genetic algorithm considering network effectiveness criteria (case study north of Kermanshah). Scientia Iranica [online]. 2023, 30(5), p. 1841-1854. ISSN 1026-3098, eISSN 2345-3605. Available from: https://doi.org/10.24200/sci.2021.55207.4116 Go to original source...
    26. FUENTES-MARILES, O. A., GRACIA-SANCHEZ, J., CHOMPA-ABARCA, J. A., DE, F. Predesign of an urban rainfall drainage network with genetic algorithms. Journal of Building Technology [online]. 2024, 6(2), p. 2717-5103. ISSN 2705-1390, eISSN 2717-5103. Available from: https://doi.org/10.32629/jbt.v6i2.2475 Go to original source...
    27. EIBEN, A. E., RAUE, P.-E., RUTTKAY, Z. Genetic algorithms with multi-parent recombination. In: International Conference on Parallel Problem Solving from Nature: proceedings [online]. Springer. 1994, p. 78-87. Available from: https://doi.org/10.1007/3-540-58484-6_252 Go to original source...
    28. ROYCHOWDHURY, S., ALLEN, T. T., ALLEN, N. B. A genetic algorithm with an earliest due date encoding for scheduling automotive stamping operations. Computers and Industrial Engineering [online]. 2017, 105, p. 201-209. ISSN 0360-8352, eISSN 1879-0550. Available from: https://doi.org/10.1016/j.cie.2017.01.007 Go to original source...
    29. EIBEN, A. E., VAN KEMENADE, C. H. Performance of multi-parent crossover operators on numerical function optimization problems. 1995.
    30. TING, C.-K., CHEN, C.-C. The effects of supermajority on multi-parent crossover. 2007.
    31. TSUTSUI, S., GHOSH, A. A study on the effect of multi-parent recombination in real coded genetic algorithms. In: 1998 IEEE International Conference on Evolutionary Computation and IEEE World Congress on Computational Intelligence (Cat. No. 98TH8360): proceedings [online]. IEEE. 1998. ISBN 0-7803-4869-9. Available from: https://doi.org/10.1109/ICEC.1998.700159 Go to original source...
    32. TSUTSUI, S., JAIN, L. C. On the effect of multi-parents recombination in binary coded genetic algorithms. In: 1998 2nd International Conference. Knowledge-Based Intelligent Electronic Systems. Proceedings KES'98 (Cat. No. 98EX111): proceedings [online]. IEEE. 1998. ISBN 0-7803-4316-6. Available from: https://doi.org/ 10.1109/KES.1998.725966 Go to original source...
    33. TING, C.-K., SU, C.-H., LEE, C.-N. Multi-parent extension of partially mapped crossover for combinatorial optimization problems. Expert Systems with Applications [online]. 2010, 37(3), p. 1879-1886. ISSN 0957-4174, eISSN 1873-6793. Available from: https://doi.org/10.1016/j.eswa.2009.07.082 Go to original source...
    34. SIN, O. C., MOIN, N. H., OMAR, M. Multi parents extended precedence preservative crossover for job shop scheduling problems. Malaysian Journal of Computer Science [online]. 2013, 26(3), p. 170-181. ISSN 0127-9084. Available from: https://ejournal.um.edu.my/index.php/MJCS/article/view/6769
    35. ARRAM, A., AYOB, M. A novel multi-parent order crossover in genetic algorithm for combinatorial optimization problems. Computers and Industrial Engineering [online]. 2019, 133, p. 267-274. ISSN 0360-8352, eISSN 1879-0550. Available from: https://doi.org/10.1016/j.cie.2019.05.012 Go to original source...
    36. DAVIS, L. Applying adaptive algorithms to epistatic domains. In: Joint International Conference on Artificial Intelligence IJCAI: proceedings. 1985. p. 162-164.
    37. ALKAFAWEEN, E., HASSANAT, A., ESSA, E., ELMOUGY, S. CSGA: a dual population genetic algorithm based on Mexican cavefish genetic diversity. Jordanian Journal of Computers and Information Technology [online]. 2024, 10(3), p. 1-16. ISSN 2413-9351, eISSN 2415-1076. Available from: https://doi.org/10.5455/jjcit.71-1707664207 Go to original source...
    38. REINELT, G. TSBLIB [online] [accessed 2024-09-01]. University of Heidelberg, 1996. Available from: http://comopt.ifi.uni-heidelberg.de/
    39. JEBARI, K., MADIAFI, M. Selection methods for genetic algorithms. International Journal of Emerging Sciences. 2013, 3(4), p. 333-344. ISSN 2222-4254.
    40. [41] DONG, M., WU, Y. Dynamic crossover and mutation genetic algorithm based on expansion sampling. In: Artificial intelligence and computational intelligence. AICI 2009. Lecture notes in computer science. Vol. 5855 [online]. DENG, H., WANG, L., WANG, F. L., LEI, J. (Eds.). Berlin, Heidelber: Springer, 2009. ISBN 978-3-642-05252-1, eISBN 978-3-642-05253-8. Available from: https://doi.org/10.1007/978-3-642-05253-8_16 Go to original source...

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