Gaussian Linear Regression Crossover for Genetic Algorithms

Document Type : Research Article

Author

Shahid Sattari Aeronautical University of Science and Technology, Tehran, Iran.

Abstract

In the simulated binary crossover, offspring are generated from parents with a coefficient of variation and uses a probability distribution function for the coefficient and there is a linear relationship between parents and offspring. Most existing methods of crossover operators generate offspring on the solution on the decision space during the search and so far, no suggestion has been proposed on making a regression model for generating the offspring on the objective space. In this paper, a Gaussian linear regression crossover has been proposed. The idea is to apply linear regression to model a relationship between parents and offspring in crossover operations through the Gaussian process. The reason for using this process is that the probability distribution of the simulated binary operator is based on the parent in the mating pool on decision space, while the probability distribution of the proposed method is on objective space in the mating pool. To optimize problems on the combinatorial sets, the proposed method is applied. The performance of the proposed algorithm was tested on Computational Expensive Optimization benchmark tests and indicates that the proposed operator is a competitive and promising approach.

Keywords

References

[1] V. K. Patel, V. J. Savsani, and M. A. Tawhid. Metaheuristic methods. In Thermal System Optimization, pages 7--32. Springer, 2019. [ bib ]
[2] M. Rostami, K. Berahmand, and S. Forouzandeh. A novel community detection based genetic algorithm for feature selection. IEEE Access, 8(1):1--27, 2021. [ bib | DOI ]
[3] M. Rostami, K. Berahmand, and S. Forouzandeh. Crossover and Mutation Operators of Genetic Algorithms. International Journal of Machine Learning and Computing, 7(1):9--12, 2017. [ bib | DOI ]
[4] A. J. Umbarkar and P. D. Sheth. CROSSOVER OPERATORS IN GENETIC ALGORITHMS: A REVIEW. ICTACT Journal on Soft Computing, 6(1), 2015. [ bib | DOI ]
[5] M. Y. Orong, A. M. Sison, and R. P. Medina. A new crossover mechanism for genetic algorithm with rank-based selection method. In 2018 5th International Conference on Business and Industrial Research (ICBIR), pages 83--88. IEEE, 2018. [ bib | DOI ]
[6] M. Sain, Y. J. Kang, and H. J. Lee. An introduction to a novel crossover operator for real-value encoded genetic algorithm: Gaussian crossover operator. In 2018 International Interdisciplinary PhD Workshop (IIPhDW), pages 85--90. IEEE, 2018. [ bib | DOI ]
[7] M. Z. Ali, N. H. Awad, P. N. Suganthan, A. M. Shatnawi, and R. G. Reynolds. An improved class of real-coded Genetic Algorithms for numerical optimization. Neurocomputing, 275:155--166, 2018. [ bib | DOI ]
[8] R. Cheng, Y. Jin, K. Narukawa, and B.Sendhoff. A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling. IEEE Transactions on Evolutionary Computation, 19(6):838 -- 856, 2015. [ bib | DOI ]
[9] K. Deb and R. B. Agrawal. Simulated Binary Crossover for Continuous Search Space. Complex Systems, 9(3):115--148, 2000. [ bib | DOI ]
[10] Y. Ding, Y.Wu, C. Huang, S. Tang, Y. Yang, L. Wei, Y. Zhuang, and Q. Tian. Learning To Learn by Jointly Optimizing Neural Architecture and Weights. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 129--138, 2022. [ bib | DOI ]
[11] M. Li and X. Yao. Quality Evaluation of Solution Sets in Multiobjective Optimisation: A Survey. ACM Computing Surveys (CSUR), 52(2):1--38, 2019. [ bib | DOI ]
[12] M. Fayyaz, H. Vahdat-nejad, and M. Kherad. Trust Inference in Social Networks by Combination of Neural Network and Genetic Algorithm. Tabriz Journal of Electrical Engineering, 50(1):331--340, 2020. [ bib | DOI ]
[13] H. Ismkhan. Novel Diversity-Preservative Strategies for Genetic Algorithms and Its Application for Large-Scale Optimization. TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, 48(2):467--479, 2018. [ bib | DOI ]
[14] B. Zamani Dehkordi and Z. Nekouei. Multi Objective Genetic Algorithm Based Ensemble Classifier Using Classification Error, Sparsity, Diversity and Density Criterion. TABRIZ JOURNAL OF ELECTRICAL ENGINEERING, 47(4):1479--1487, 2018. [ bib | DOI ]
Journal of Computing and Security
Volume 9, Issue 1 - Serial Number 1
January 2022
Pages 45-56

Files

History

  • Receive Date: 30 October 2021
  • Revise Date: 22 April 2022
  • Accept Date: 22 May 2022

Share

How to cite

Statistics