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Link to original content: https://doi.org/10.1007/978-3-030-29859-3_17
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Haploid Versus Diploid Genetic Algorithms. A Comparative Study

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Hybrid Artificial Intelligent Systems (HAIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11734))

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Abstract

Genetic algorithms (GAs) are powerful tools for solving complex optimization problems, usually using a haploid representation. In the past decades, there has been a growing interest concerning the diploid genetic algorithms. Even though this area seems to be attractive, it lacks wider coverage and research in the Evolutionary Computation community. The scope of this paper is to provide some reasons why this situation happens and in order to fulfill this aim, we present experimental results using a conventional haploid GA and a developed diploid GA tested on some major benchmark functions used for performance evaluation of genetic algorithms. The obtained results show the superiority of the diploid GA over the conventional haploid GA in the case of the considered benchmark functions.

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References

  1. Back, T., Fogel, D.B., Michalewicz, Z. (eds.): Basic Algorithms and Operators, 1st edn. IOP Publishing Ltd., Bristol (1999)

    Google Scholar 

  2. Bhasin, H., Behal, G., Aggarwal, N., Saini, R.K., Choudhary, S.: On the applicability of diploid genetic algorithms in dynamic environments. Soft. Comput. 20(9), 3403–3410 (2016). https://doi.org/10.1007/s00500-015-1803-5

    Article  Google Scholar 

  3. Bull, L.: Haploid-diploid evolutionary algorithms: the Baldwin effect and recombination nature’s way. In: AISB (2017)

    Google Scholar 

  4. Digalakis, J., Margaritis, K.: On benchmarking functions for genetic algorithms. Int. J. Comput. Math. 77(4), 481–506 (2001). https://doi.org/10.1080/00207160108805080

    Article  MathSciNet  MATH  Google Scholar 

  5. 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 

  6. Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975). Second edition 1992

    Google Scholar 

  7. Liekens, A., Eikelder, H., Hilbers, P.: Modeling and simulating diploid simple genetic algorithms. In: Proceedings Foundations of Genetic Algorithms VII, pp. 151–168. FOGA VII (2003)

    Google Scholar 

  8. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  9. 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 (1995). http://dl.acm.org/citation.cfm?id=645514.657904

  10. 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 

  11. Pop, P., 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 

  12. 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, GECCO 2018, pp. 340–346. ACM, New York (2018). https://doi.org/10.1145/3205455.3205545

  13. Yang, S., Yao, X.: Experimental study on population-based incremental learning algorithms for dynamic optimization problems. Soft Comput. 9(11), 815–834 (2005). https://doi.org/10.1007/s00500-004-0422-3

    Article  MATH  Google Scholar 

  14. Yukiko, Y., Nobue, A.: A diploid genetic algorithm for preserving population diversity — Pseudo-Meiosis GA. In: Davidor, Y., Schwefel, H.-P., Männer, R. (eds.) PPSN 1994. LNCS, vol. 866, pp. 36–45. Springer, Heidelberg (1994). https://doi.org/10.1007/3-540-58484-6_248

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Technical University of Cluj-Napoca, North University Center of Baia Mare, Cluj-Napoca, Romania

    Adrian Petrovan, Petrica Pop-Sitar & Oliviu Matei

Authors
  1. Adrian Petrovan
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  2. Petrica Pop-Sitar
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  3. Oliviu Matei
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Corresponding author

Correspondence to Adrian Petrovan .

Editor information

Editors and Affiliations

  1. University of León, León, Spain

    Hilde Pérez García

  2. University of León, León, Spain

    Lidia Sánchez González

  3. University of León, León, Spain

    Manuel Castejón Limas

  4. University of A Coruña, Ferrol, Spain

    Héctor Quintián Pardo

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado Rodríguez

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Petrovan, A., Pop-Sitar, P., Matei, O. (2019). 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) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_17

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  • DOI: https://doi.org/10.1007/978-3-030-29859-3_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29858-6

  • Online ISBN: 978-3-030-29859-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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