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Mexican International Conference on Artificial Intelligence

MICAI 2019: Advances in Soft Computing pp 330–349Cite as

Solving Dynamic Combinatorial Optimization Problems Using a Probabilistic Distribution as Self-adaptive Mechanism in a Genetic Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11835))

Abstract

In recent years, the interest to solve dynamic combinatorial optimization problems has increased. Metaheuristic algorithms have been used to find good solutions in a reasonably low time, in addition, the use of self-adaptive strategies has increased considerably because they have proved to be a good option to improve performance in these algorithms. In this research, a self-adaptive mechanism is developed to improve the performance of the genetic algorithm for dynamic combinatorial problems, using the strategy of genotype-phenotype mapping and probabilistic distributions. Results demonstrate the capability of the mechanism to help algorithms to adapt in dynamic environments.

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Author information

Authors and Affiliations

  1. Instituto Mexicano del Transporte, Querétaro, Mexico

    Cesar J. Montiel Moctezuma

  2. Tecnologico de Monterrey, Monterrey, Mexico

    Jaime Mora & Miguel Gonzalez-Mendoza

Authors
  1. Cesar J. Montiel Moctezuma
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  2. Jaime Mora
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  3. Miguel Gonzalez-Mendoza
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Corresponding author

Correspondence to Miguel Gonzalez-Mendoza .

Editor information

Editors and Affiliations

  1. Universidad Panamericana, Mexico City, Mexico

    Lourdes Martínez-Villaseñor

  2. Instituto Politecnico Nacional, Mexico, Mexico

    Ildar Batyrshin

  3. Universidad Veracruzana, Xalapa, Mexico

    Antonio Marín-Hernández

Appendix A

Appendix A

Fig. A1.
figure 12

Best solutions for static One-Max problem (50 bits);

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Fig. A2.
figure 13

Average solutions for static One-Max problem (50 bits)

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Fig. A3.
figure 14

Best solutions for dynamic One-Max problem (50 bits);

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Fig. A4.
figure 15

Average solutions for dynamic One-Max problem (50 bits)

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Fig. A5.
figure 16

Best solutions for static TSP problem (ulysses22);

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Fig. A6.
figure 17

Average solutions for static TSP problem (ulysses22)

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Fig. A7.
figure 18

Average solutions for dynamic TSP problem (ulysses22);

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Fig. A8.
figure 19

Average solutions for dynamic TSP problem (ulysses22)

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Fig. A9.
figure 20

Best-average results for static One-Max problem (maximization);

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Fig. A10.
figure 21

Best-average results for dynamic One-Max problem (maximization)

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Fig. A11.
figure 22

Best-average results for static TSP problem (minimization);

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Fig. A12.
figure 23

Best-average results for dynamic TSP problem (minimization)

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Montiel Moctezuma, C.J., Mora, J., Gonzalez-Mendoza, M. (2019). Solving Dynamic Combinatorial Optimization Problems Using a Probabilistic Distribution as Self-adaptive Mechanism in a Genetic Algorithm. In: Martínez-Villaseñor, L., Batyrshin, I., Marín-Hernández, A. (eds) Advances in Soft Computing. MICAI 2019. Lecture Notes in Computer Science(), vol 11835. Springer, Cham. https://doi.org/10.1007/978-3-030-33749-0_27

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  • DOI: https://doi.org/10.1007/978-3-030-33749-0_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33748-3

  • Online ISBN: 978-3-030-33749-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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