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Granular Box Regression Using Simulated Annealing and Genetic Algorithm: A Comparative Study

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Abstract

Representation of compound information in a truthful, coarse way forms the layout of the granular computing paradigm. In granular computing, the continuous variables are mapped into intervals to be utilized in the extraction of fuzzy graphs from the given dataset. The objective of Granular Box Regression is to establish a relationship between the predictor and the target variables using multidimensional boxes. However, the traditional box regression technique uses a greedy approach due to which the algorithm tends to converge to some local optima, and optimal box configuration may not be obtained. In this article, we suggest overcoming the problem of getting stuck into local optima using randomized search and optimization techniques of Simulated Annealing and Genetic Algorithms. The major advantage of using Simulated Annealing is that it allows occasional acceptance of poor solutions to avoid getting trapped into some local optima. Genetic Algorithms also provide efficient, robust search optimization techniques that minimize the chances of a local optimum problem. A comparative analysis is conducted while implementing Granular Box Regression using Simulated Annealing and Genetic Algorithm, and the results are demonstrated on some artificial datasets, economic datasets, and some datasets of COVID-19 cases. As per the quantitative analysis of the Granular Box Regression (GBR) methods, both GBR-SA and GBR-GA significantly outperformed the baseline GBR algorithm across various datasets. For instance in the 3DED1 dataset, GBR-SA and GBR-GA showed an improvement of \(16.02\%\) while for 2DCD2, they showed an improvement of \(22.22\%\). However, the execution time varied for GBR-GA by \(95.4\%\) with the GBR-SA algorithm, which in turn took about \(26\%\) average time more than the base algorithm of GBR.

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Availability of Data and Materials

The publicly available datasets analysed during the study have been mentioned in Sects. 4.2 and 4.3 respectively. The datasets generated during the current study are not publicly available due to some technical constraints but are available from the corresponding author on reasonable request and may be accessed accordingly.

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Funding

M. Chakraborty and U. Maulik acknowledges the support received from the Indo-French Centre for the Promotion of Advanced Research, Ministry of Science and Technology, Govt. of India through the project: 6702-1 ‘Exploring Graph Neural Networks (GNN’s) for Data-Driven Modeling of Poly-Pharmacy Adverse Drug Events from drug-drug interactions’.  A. Mukhopadhyay acknowledges the support received from the MATRICS project grant MTR/2020/000326 of DST-SERB, Govt. of India.

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All authors contributed to the study conception and design. Material preparation and concept, data collection and analysis were performed by Mrittika Chakraborty, Ujjwal Maulik and Anirban Mukhopadhyay. The first draft of the manuscript was written by Mrittika Chakraborty and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Mrittika Chakraborty.

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Chakraborty, M., Maulik, U. & Mukhopadhyay, A. Granular Box Regression Using Simulated Annealing and Genetic Algorithm: A Comparative Study. SN COMPUT. SCI. 5, 978 (2024). https://doi.org/10.1007/s42979-024-03333-y

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