A novel hybrid genetic algorithm-based firefly mating algorithm for solving Sudoku

Jana, Sunanda; Dey, Anamika; Maji, Arnab Kumar; Pal, Rajat Kumar

doi:10.1007/s11334-021-00397-4

Cited by 7 publications

(2 citation statements)

References 10 publications

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“…GA tends to converge prematurely at local optima [16]. Thus, Jana et al [17] developed a new hybrid technique where the firefly mating algorithm was embedded with GA. The hybridisation was done primarily to control the premature convergence at local optima.…”

Section: Literature Surveymentioning

confidence: 99%

Design and Analysis of a Modified 3D Sudoku Solver

Jana

Mallik

Khan³

et al. 2023

IEEE Access

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Sudoku is a complicated multidimensional mathematical structure with several applications in various computer science domains. 3D Sudoku, compared to 2D, has one more dimension that can potentially provide an extra edge to the different applications of this puzzle game. Various researchers have developed various types of 2D Sudoku solver using different methodologies. But there is very limited research in the area of developing 3D Sudoku solver. We have proposed two different solvers for solving 3D Sudoku puzzles of size 9×9×9. Both the solvers providing all possible solutions for a 3D Sudoku instance. 2D Sudoku puzzles are applied in different research domains with different purposes. Recently 3D structure of Sudoku is also applying in several areas to achieve more effectiveness compared to 2D Sudoku. As well as it can also be used to solve problem in 3D space. Again, solving an NP-complete puzzle by considering its 3D structure is a challenging job. Thus, we have endeavoured to achieve all possible solutions for a 3D Sudoku instance in this work. In the first version of our proposed algorithm all probable values for each blank cell have been computed and stored. Subsequently, few elimination-based methods have been used to reduce the number of probable values (if possible) for each blank cell. Finally, the solutions have been computed using the backtracking method. In the second version of our proposed algorithm, the nine 2D Sudoku puzzles lying in the xz-plane one above the other, which form the 3D puzzle, have been fed as the input. All possible solutions have been obtained for each of the nine puzzles. Then, the obtained solutions have been mapped to achieve one or more solutions for the 3D Sudoku instance. Thus, our proposed techniques provide a new approach for solving 3D Sudoku. In addition, applying the obtained solutions provides us with an advantage over 2D Sudoku, in solving problem of 3D space and where more data is required.

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Section: Literature Surveymentioning

confidence: 99%

Design and Analysis of a Modified 3D Sudoku Solver

Jana

Mallik

Khan³

et al. 2023

IEEE Access

Self Cite

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“…Also, firefly-based algorithms are more effective than genetic algorithm on finding globally optimal solutions. Existing literature provides comparisons in this regard [42][43][44][45][46]. We will provide a detailed comparative analysis in Section 5.…”

Section: Gauss Mutationmentioning

confidence: 99%

Adversarial example‐based test case generation for black‐box speech recognition systems

Cai

Zhang

Dong

et al. 2023

Software Testing Verif & Rel

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Test case generation techniques based on adversarial examples are commonly used to enhance the reliability and robustness of image-based and text-based machine learning applications. However, efficient techniques for speech recognition systems are still absent. This paper proposes a family of methods that generate targeted adversarial examples for speech recognition systems. All are based on the firefly algorithm (F), and are enhanced with gauss mutations and / or gradient estimation (F-GM, F-GE, F-GMGE) to fit the specific problem of targeted adversarial test case generation. We conduct an experimental evaluation on three different types of speech datasets, including Google Command, Common Voice and LibriSpeech. In addition, we recruit volunteers to evaluate the performance of the adversarial examples. The experimental results show that, compared with existing approaches, these approaches can effectively improve the success rate of the targeted adversarial example generation. The code is publicly available at https://github.com/HanboCai/FGMGE.

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