The Applicability of Genetic Algorithm in Cryptanalysis: A Survey

Khan, Asif; Lone, Auqib Hamid; Badroo, Firdoos Ahmad

doi:10.5120/ijca2015907118

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…Each particle in the search space of a given problem is encrypted using a schema to display the given problem in the form of a series of fixed-length characters. The GA tries to find the best solution to the problem with the help of genetic fertilisation of members of a population over a number of generations (Hadji and Gaubert, 2015; Thengade and Dondal, 2012; Mukhopadadhyay and Balitanas, 2009). In this work, the objective function is defined as the reciprocal absolute value of the power difference, because the GA algorithm is defined as maximisation process, and its presentation is shown in equation (1): where = P mpp,GA is the calculated maximum power by the GA algorithm and P mpp,catalog is the experimentally determined value given by the producer for given conditions.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Stochastic approach for maximum power point determination for bifacial PV modules by implementing two different cell models

Najdoska,

Cvetkovski

2024

COMPEL

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Purpose This paper presents the determination of the maximum power point of a bifacial photovoltaic (PV) system using two different cell models. The optimal power point is determined by using genetic algorithm (GA), as an optimisation tool. The purpose of this paper is to find which of the two analysed models gives better results in the determination of the maximum power point of a bifacial PV system for different solar irradiations. The quality of the results gained from both models is analysed based on the value of the objective function. Design/methodology/approach In this research work, the maximum power point of bifacial PV modules is determined by using two different PV cell models, such as the simplified and two-diode models of PV cells. Based on the input electrical data for the analysed bifacial PV module as well as the mathematical model of the two PV cell presentations, the values for the current and the voltage at the maximum power point for a given solar irradiation and working temperature are determined by the algorithm for each solution in the population and generation. Findings From the presented results and the performed analysis, it can be concluded that GA is quite appropriate for this purpose and gives adequate results for both models and for all working conditions. The two-diode model was found to be more suitable compared with the simplified model due to its complexity. Therefore, although the power difference for each of the scenarios for the two compared models does not differ significantly among the two models, it is in favour of the two-diode model. Which implicates that the for fast and simple calculation the simplified model can also do the job. Practical implications This approach can be very successfully applied in the design process of a PV plant to forecast the output characteristics of the PV system if there is enough information about the weather conditions for a given location. This procedure can be very helpful in the process of selection of right PV module and inverter for a given location. Originality/value An optimisation technique using GA as an optimisation tool has been developed and successfully applied in the determination of the maximum power point for a bifacial PV module using to different models of solar cell. The results are compared with the analytically determined values as well as with the values given from the producer and they show good agreement.

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Section: Genetic Algorithmmentioning

confidence: 99%

Stochastic approach for maximum power point determination for bifacial PV modules by implementing two different cell models

Najdoska,

Cvetkovski

2024

COMPEL

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“…In [3], Hameed, Asif, et al presents a methodical survey of Genetic Algorithm connected to the cryptanalysis. To comprehend the extent of GAs in cryptanalysis, it gives an efficient audit.…”

Section: Introductionmentioning

confidence: 99%

An improved fitness function for automated cryptanalysis using genetic algorithm

Forhad

Hossain

Rahman

et al. 2019

IJEECS

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Genetic Algorithm (GA) is a popular desire for the researchers for creating an automated cryptanalysis system. GA strategy is useful for many problems. Genetic Algorithms try to solve problems by using genetic processes. Different techniques for deciding on fitness function relying on the ciphers have proposed by different researchers. The most necessary component is to set such a fitness function that can evaluate different types of ciphers on the identical scale. In this paper, we have proposed a combined fitness function that is valid for great sorts of ciphers. We use GA to select the fitness function. We have bought the higher result after imposing our proposed method.

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Study of Parameters in the Genetic Algorithm for the Attack on Block Ciphers

et al. 2021

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In recent years, the use of Genetic Algorithms (GAs) in symmetric cryptography, in particular in the cryptanalysis of block ciphers, has increased. In this work, the study of certain parameters that intervene in GAs was carried out, such as the time it takes to execute a certain number of iterations, so that a number of generations to be carried out in an available time can be estimated. Accordingly, the size of the set of individuals that constitute admissible solutions for GAs can be chosen. On the other hand, several fitness functions were introduced, and which ones led to better results was analyzed. The experiments were performed with the block ciphers AES(t), for t∈{3,4,7}.

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The Applicability of Genetic Algorithm in Cryptanalysis: A Survey

Cited by 3 publications

References 26 publications

Stochastic approach for maximum power point determination for bifacial PV modules by implementing two different cell models

Stochastic approach for maximum power point determination for bifacial PV modules by implementing two different cell models

An improved fitness function for automated cryptanalysis using genetic algorithm

Study of Parameters in the Genetic Algorithm for the Attack on Block Ciphers

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