GARA: A novel approach to Improve Genetic Algorithms' Accuracy and Efficiency by Utilizing Relationships among Genes (2404.18955v1)
Abstract: Genetic algorithms have played an important role in engineering optimization. Traditional GAs treat each gene separately. However, biophysical studies of gene regulatory networks revealed direct associations between different genes. It inspires us to propose an improvement to GA in this paper, Gene Regulatory Genetic Algorithm (GRGA), which, to our best knowledge, is the first time to utilize relationships among genes for improving GA's accuracy and efficiency. We design a directed multipartite graph encapsulating the solution space, called RGGR, where each node corresponds to a gene in the solution and the edge represents the relationship between adjacent nodes. The edge's weight reflects the relationship degree and is updated based on the idea that the edges' weights in a complete chain as candidate solution with acceptable or unacceptable performance should be strengthened or reduced, respectively. The obtained RGGR is then employed to determine appropriate loci of crossover and mutation operators, thereby directing the evolutionary process toward faster and better convergence. We analyze and validate our proposed GRGA approach in a single-objective multimodal optimization problem, and further test it on three types of applications, including feature selection, text summarization, and dimensionality reduction. Results illustrate that our GARA is effective and promising.
- J. Holland, “adaptation in natural and artificial systems, university of michigan press, ann arbor,”,” Cité page, vol. 100, p. 33, 1975.
- R. W. Doran, “The gray code,” Journal of Universal Computer Science, vol. 13, no. 11, pp. 1573–1597, 2007.
- I. Korejo, S. Yang, K. Brohi, and Z. Khuhro, “Multi-population methods with adaptive mutation for multi-modal optimization problems,” 2013.
- C. Qian, Y. Yu, and Z.-H. Zhou, “An analysis on recombination in multi-objective evolutionary optimization,” in Proceedings of the 13th annual conference on Genetic and evolutionary computation, 2011, pp. 2051–2058.
- T. D. Pham and W.-K. Hong, “Genetic algorithm using probabilistic-based natural selections and dynamic mutation ranges in optimizing precast beams,” Computers & Structures, vol. 258, p. 106681, 2022.
- F. Xie, Q. Sun, Y. Zhao, H. Du et al., “An improved directed crossover genetic algorithm based on multilayer mutation,” Journal of Control Science and Engineering, vol. 2022, 2022.
- T.-P. Hong, Y.-C. Lee, and M.-T. Wu, “An effective parallel approach for genetic-fuzzy data mining,” Expert Systems with Applications, vol. 41, no. 2, pp. 655–662, 2014.
- M. Javidi and R. Hosseinpourfard, “Chaos genetic algorithm instead genetic algorithm.” International Arab Journal of Information Technology (IAJIT), vol. 12, no. 2, 2015.
- T. A. El-Mihoub, A. A. Hopgood, L. Nolle, and A. Battersby, “Hybrid genetic algorithms: A review.” Eng. Lett., vol. 13, no. 2, pp. 124–137, 2006.
- M. Srinivas and L. M. Patnaik, “Adaptive probabilities of crossover and mutation in genetic algorithms,” IEEE Transactions on Systems, Man, and Cybernetics, 1994.
- M. Remm, C. E. Storm, and E. L. Sonnhammer, “Automatic clustering of orthologs and in-paralogs from pairwise species comparisons,” Journal of molecular biology, vol. 314, no. 5, pp. 1041–1052, 2001.
- X. Lai, R. Blanc-Mathieu, L. GrandVuillemin, Y. Huang, A. Stigliani, J. Lucas, E. Thévenon, J. Loue-Manifel, L. Turchi, H. Daher et al., “The leafy floral regulator displays pioneer transcription factor properties,” Molecular Plant, vol. 14, no. 5, pp. 829–837, 2021.
- M. S. Islam and M. R. Islam, “A hybrid framework based on genetic algorithm and simulated annealing for rna structure prediction with pseudoknots,” Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 3, pp. 912–922, 2022.
- K. Yadav, B. Kumar, J. M. Guerrero, and A. Lashab, “A hybrid genetic algorithm and grey wolf optimizer technique for faster global peak detection in pv system under partial shading,” Sustainable Computing: Informatics and Systems, vol. 35, p. 100770, 2022.
- W. Xu, Y. Hu, W. Luo, L. Wang, and R. Wu, “A multi-objective scheduling method for distributed and flexible job shop based on hybrid genetic algorithm and tabu search considering operation outsourcing and carbon emission,” Computers & Industrial Engineering, vol. 157, p. 107318, 2021.
- Y. Xue, H. Zhu, J. Liang, and A. Słowik, “Adaptive crossover operator based multi-objective binary genetic algorithm for feature selection in classification,” Knowledge-Based Systems, vol. 227, p. 107218, 2021.
- L. M. Hvattum, “Adjusting the order crossover operator for capacitated vehicle routing problems,” Computers & Operations Research, vol. 148, p. 105986, 2022.
- L. Manzoni, L. Mariot, and E. Tuba, “Balanced crossover operators in genetic algorithms,” Swarm and Evolutionary Computation, vol. 54, p. 100646, 2020.
- J.-H. Yi, S. Deb, J. Dong, A. H. Alavi, and G.-G. Wang, “An improved nsga-iii algorithm with adaptive mutation operator for big data optimization problems,” Future Generation Computer Systems, vol. 88, pp. 571–585, 2018.
- M. G. Altarabichi, S. Nowaczyk, S. Pashami, and P. S. Mashhadi, “Fast genetic algorithm for feature selection — a qualitative approximation approach,” Expert Systems with Applications, vol. 211, p. 118528, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0957417422016049
- M. Mojrian and S. A. Mirroshandel, “A novel extractive multi-document text summarization system using quantum-inspired genetic algorithm: Mtsqiga,” Expert systems with applications, vol. 171, p. 114555, 2021.
- N. Radeev, “Transparent dimension reduction by feature construction with genetic algorithm,” Authorea Preprints, 2023.