iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://doi.org/10.1007/978-3-031-74183-8_27
Diversity Population Metrics in Diploid and Haploid Genetic Algorithm Variants | SpringerLink
Skip to main content
Springer Nature Link
Log in

Diversity Population Metrics in Diploid and Haploid Genetic Algorithm Variants

  • Conference paper
  • First Online:
Hybrid Artificial Intelligent Systems (HAIS 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14857))

Included in the following conference series:

  • 104 Accesses

Abstract

Genetic Algorithms (GAs) are powerful optimization techniques inspired by the principles of natural selection and genetics. One critical aspect of their success lies in the diversity of solutions within their populations. Diversity ensures exploration of a broader solution space, preventing premature convergence to sub-optimal solutions. Consequently, the evaluation and maintenance of diversity metrics in GA populations have garnered significant attention in the field of evolutionary computation. This article presents the efficiency of diploid genetic algorithms against the haploid variant through measures of population diversity.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Al-Rahman, D.I.A., Attea, B.A.: Solving multiple-container packing problems using pseudo-meiosis genetic algorithm. J. Eng. 11(3), 455–466 (2005)

    Article  Google Scholar 

  2. Attea, B.A.: The effect of pseudo-meiosis genetic algorithm on bit-coding stationary genetic search. Iraqi J. Sci. 47(1), 160–165 (2006)

    Google Scholar 

  3. Burks, A.R., Punch, W.F.: An efficient structural diversity technique for genetic programming. In: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 991–998 (2015)

    Google Scholar 

  4. Cheng, C., Lee, W., Miltenburg, J.: A bi-chromosome genetic algorithm for minimizing intercell and intracell moves. Group Technology and Cellular Manufacturing: A State-of-the-Art Synthesis of Research and Practice, pp. 205–219 (1998)

    Google Scholar 

  5. Cobb, H.G., Grefenstette, J.J.: Genetic algorithms for tracking changing environments. Technical Report, Naval Research Lab Washington DC (1993)

    Google Scholar 

  6. Collingwood, E., Corne, D., Ross, P.: Useful diversity via multiploidy. In: Proceedings of IEEE International Conference on Evolutionary Computation, pp. 810–813. IEEE (1996)

    Google Scholar 

  7. Cruz-Chávez, M.A., Martínez-Oropeza, A., et al.: Feasible initial population with genetic diversity for a population-based algorithm applied to the vehicle routing problem with time windows. Math. Probl. Eng. 2016, 3851520 (2016)

    Google Scholar 

  8. Darwin, C.: On the Origin of Species by Means of Natural Selection, or the Preservation of Favoured Races in the Struggle for life. Murray, London (1859)

    Google Scholar 

  9. De Jong, K.A.: An Analysis of the Behavior of a Class of Genetic Adaptive Systems. University of Michigan (1975)

    Google Scholar 

  10. Deb, K., Goldberg, D.E.: An investigation of niche and species formation in genetic function optimization. In: Proceedings of the Third International Conference on Genetic Algorithms, pp. 42–50 (1989)

    Google Scholar 

  11. Dieterich, J.M., Hartke, B.: Empirical review of standard benchmark functions using evolutionary global optimization. arXiv preprint arXiv:1207.4318 (2012)

  12. Dulebenets, M.A.: A diploid evolutionary algorithm for sustainable truck scheduling at a cross-docking facility. Sustainability 10(5), 1333 (2018)

    Article  Google Scholar 

  13. Dulebenets, M.A.: An adaptive polyploid memetic algorithm for scheduling trucks at a cross-docking terminal. Inf. Sci. 565, 390–421 (2021)

    Article  MathSciNet  Google Scholar 

  14. Galán, S.F., Mengshoel, O.J., Pinter, R.: A novel mating approach for genetic algorithms. Evol. Comput. 21(2), 197–229 (2013)

    Article  Google Scholar 

  15. Goldberg, D., Smith, R.: Nonstationary function optimization using genetic algorithms with dominance and diploidy. In: Proceedings of Second International Conference on Genetic Algorithms and their Application, pp. 59–68 (1987)

    Google Scholar 

  16. of Health, N.I., et al.: Talking glossary of genetic terms. national human genome research institute web site (2017)

    Google Scholar 

  17. Herrera-Poyatos, A., Herrera, F.: Genetic and memetic algorithm with diversity equilibrium based on greedy diversification. arXiv preprint arXiv:1702.03594 (2017)

  18. Hillis, W.D.: Co-evolving parasites improve simulated evolution as an optimization procedure. Physica D 42(1–3), 228–234 (1990)

    Article  Google Scholar 

  19. Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  20. Jamil, M., Yang, X.S.: A literature survey of benchmark functions for global optimization problems. arXiv preprint arXiv:1308.4008 (2013)

  21. John, B.: Meiosis, vol. 22. Cambridge University Press, Cambridge (2005)

    Google Scholar 

  22. Li, X., Epitropakis, M.G., Deb, K., Engelbrecht, A.: Seeking multiple solutions: an updated survey on niching methods and their applications. IEEE Trans. Evol. Comput. 21(4), 518–538 (2016)

    Article  Google Scholar 

  23. Lin, S.K.: Gibbs paradox and the concepts of information, symmetry, similarity and their relationship. Entropy 10(1), 1–5 (2008)

    Article  Google Scholar 

  24. Long, Q., Wu, C., Wang, X., Jiang, L., Li, J., et al.: A multiobjective genetic algorithm based on a discrete selection procedure. Math. Prob. Eng. 2015, 349781 (2015)

    Google Scholar 

  25. Matei, O.: Evolutionary computation: principles and practices. Risoprint (2008)

    Google Scholar 

  26. Matei, O., Pop, P.C., Sas, J.L., Chira, C.: An improved immigration memetic algorithm for solving the heterogeneous fixed fleet vehicle routing problem. Neurocomputing 150, 58–66 (2015)

    Article  Google Scholar 

  27. Michael, R., Vida, K., Shuvr, S.: A modular genetic algorithm for scheduling task graphs. United States (2003)

    Google Scholar 

  28. Michalewicz, Z.: Genetic algorithms+ data structures= evolution programs. Springer Science & Business Media (2013)

    Google Scholar 

  29. Ng, K.P., Wong, K.C.: A new diploid scheme and dominance change mechanism for non-stationary function optimization. In: Proceedings of the 6th International Conference on Genetic Algorithms, pp. 159–166. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1995). http://dl.acm.org/citation.cfm?id=645514.657904

  30. Pencheva, T., Atanassov, K., Shannon, A.: Modelling of a roulette wheel selection operator in genetic algorithms using generalized nets. Int. J. Bioautomation 13(4), 257 (2009)

    Google Scholar 

  31. Petrovan, A., Matei, O., Erdei, R.: A Behavioural Study of the Crossover Operator in Diploid Genetic Algorithms. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds.) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020), SOCO 2020, Advances in Intelligent Systems and Computing, vol. 1268, pp 79–88. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-57802-2_8

  32. Petrovan, A., Pop-Sitar, P., Matei, O.: Haploid versus diploid genetic algorithms. a comparative study. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds.) HAIS 2019. LNCS (LNAI), vol. 11734, pp. 193–205. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29859-3_17

    Chapter  Google Scholar 

  33. Pop, P., Matei, O., Pintea, C.: A two-level diploid genetic based algorithm for solving the family traveling salesman problem. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 340–346. ACM (2018)

    Google Scholar 

  34. Pop, P., Oliviu, M., Sabo, C.: A Hybrid diploid genetic based algorithm for solving the generalized traveling salesman problem. In: Martínez de Pisón, F.J., Urraca, R., Quintián, H., Corchado, E. (eds.) HAIS 2017. LNCS (LNAI), vol. 10334, pp. 149–160. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59650-1_13

    Chapter  Google Scholar 

  35. Pop, P.C., Matei, O., Sabo, C., Petrovan, A.: A two-level solution approach for solving the generalized minimum spanning tree problem. Eur. J. Oper. Res. 265(2), 478–487 (2018)

    Article  MathSciNet  Google Scholar 

  36. Rieger, R., Michaelis, A., Green, M.M.: Glossary of Genetics: Classical and Molecular. Springer Science & Business Media (2012)

    Google Scholar 

  37. Rui, L., Qin, Y., Li, B., Gao, Z.: Context-based intelligent scheduling and knowledge push algorithms for AR-assist communication network maintenance. Comput. Model. Eng. Sci. 118(2), 291–315 (2019)

    Google Scholar 

  38. Schlierkamp-Voosen, D., Mühlenbein, H.: Strategy adaptation by competing subpopulations. In: Davidor, Y., Schwefel, HP., Männer, R. (eds.) Parallel Problem Solving from Nature - PPSN III, PPSN 1994, LNCS, vol. 866. Springer, Berlin, Heidelberg (1994). https://doi.org/10.1007/3-540-58484-6_264

  39. Schwefel, H.P.: Numerical Optimization of Computer Models. Wiley, Inc, Hoboken (1981)

    Google Scholar 

  40. Thorgaard, G.H.: Ploidy manipulation and performance. Aquaculture 57(1–4), 57–64 (1986)

    Article  Google Scholar 

  41. Witten, E.: A mini-introduction to information theory. La Rivista del Nuovo Cimento 43(4), 187–227 (2020)

    Article  Google Scholar 

  42. Wong, Y.Y., Lee, K.H., Leung, K.S., Ho, C.W.: A novel approach in parameter adaptation and diversity maintenance for genetic algorithms. Soft. Comput. 7, 506–515 (2003)

    Article  Google Scholar 

  43. Wu, X., Chu, C.H., Wang, Y., Yan, W.: Concurrent design of cellular manufacturing systems: a genetic algorithm approach. Int. J. Prod. Res. 44(6), 1217–1241 (2006)

    Article  Google Scholar 

  44. Younes, A., Basir, O., Calamai, P.: A hybrid evolutionary approach for combinatorial problems in dynamic environments. In: 2006 Canadian Conference on Electrical and Computer Engineering, pp. 1595–1600. IEEE (2006)

    Google Scholar 

  45. Yu, Z., Ni, M., Wang, Z., Zhang, Y., et al.: Dynamic route guidance using improved genetic algorithms. Math. Probl. Eng. 2013, 765135 (2013)

    Google Scholar 

  46. Yukiko, Y., Nobue, A.: A diploid genetic algorithm for preserving population diversity - Pseudo-Meiosis GA. In: Davidor, Y., Schwefel, HP., Männer, R. (eds.) Parallel Problem Solving from Nature - PPSN III, PPSN 1994, LNCS, vol. 866, pp 36–45. Springer, Berlin (1994). https://doi.org/10.1007/3-540-58484-6_248

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Electronics and Computer Science, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

    Adrian Petrovan & Oliviu Matei

  2. Department of Mathematics and Computer Science, Technical University of Cluj-Napoca, Cluj-Napoca, Romania

    Petrica C. Pop & Cosmin Sabo

Authors
  1. Adrian Petrovan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Oliviu Matei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Petrica C. Pop
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cosmin Sabo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Adrian Petrovan .

Editor information

Editors and Affiliations

  1. University of A Coruña - CITIC, Ferrol, La Coruña, Spain

    Héctor Quintián

  2. University of Salamanca, Salamanca, Spain

    Emilio Corchado

  3. Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  4. University of León, León, Spain

    Hilde Pérez García

  5. University of A Coruña - CITIC, Ferrol, La Coruña, Spain

    Esteban Jove Pérez

  6. University of A Coruña - CITIC, Ferrol, La Coruña, Spain

    José Luis Calvo Rolle

  7. University of La Rioja, Logroño, La Rioja, Spain

    Francisco Javier Martínez de Pisón

  8. University of Deusto Avda, Bilbao, Spain

    Pablo García Bringas

  9. Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  10. University of Burgos, Burgos, Spain

    Álvaro Herrero

  11. University of Bergamo, Bergamo, Bergamo, Italy

    Paolo Fosci

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Petrovan, A., Matei, O., Pop, P.C., Sabo, C. (2025). Diversity Population Metrics in Diploid and Haploid Genetic Algorithm Variants. In: Quintián, H., et al. Hybrid Artificial Intelligent Systems. HAIS 2024. Lecture Notes in Computer Science(), vol 14857. Springer, Cham. https://doi.org/10.1007/978-3-031-74183-8_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-74183-8_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-74182-1

  • Online ISBN: 978-3-031-74183-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation