Adaptive Genetic Algorithm (AGA) Based Optimal Directed Random Testing for Reducing Interactive Faults

Rao, K. Koteswara; Y, Saroja Mrs; Babu, N. Ramesh; Kumari, G. Lalitha; Y, Surekha Mrs

doi:10.21817/indjcse/2021/v12i2/211202170

Cited by 4 publications

(2 citation statements)

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“…The advantage of this is that it can solve the optimization problem of implicit indicators [ 19 , 20 ]. However, due to human subjective factors and the increase of fatigue, the same individual may have different adaptive values in different evaluation stages [ 21 , 22 ]. The existence of model deviation will lead to slow evolution speed and relatively low satisfaction of final results and then lead to long evaluation time, resulting in fatigue problem.…”

Section: The Basic Research Content Of Interactive Genetic Algorithmmentioning

confidence: 99%

Exploration of Landscape Lighting Design Based on Interactive Genetic Algorithm

Shi

Zheng

2022

Computational and Mathematical Methods in Medicine

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There are many problems in the practical application of landscape lighting design. In order to solve these problems more specifically, based on the relevant theories of interactive genetic algorithm, radial basis function and hesitation degree are introduced into genetic algorithm. Through the analysis and processing of the data to get the optimized interactive genetic algorithm, the algorithm can analyze and optimize the landscape lighting design. Based on this model, the lighting design can be predicted and analyzed, and the prediction result is relatively good. Relevant studies show that the interactive genetic algorithm can be divided into three typical change stages according to the different results of intensity calculation, of which the first stage mainly presents the trend of gradual decline. The fluctuation phenomenon is obvious in the second paragraph. The third paragraph shows a gradual increasing trend of change. The corresponding relationship between the two fitness functions is obvious. With the increase of experts in independent variables, the corresponding fitness values show a trend of gradual decline on the whole. Through the calculation and analysis of five different indicators of landscape lighting by using interactive genetic algorithm, it can be seen that electrification has a relatively small impact on landscape lighting. The results of intelligent and environmental protection calculation are relatively high, and the corresponding range of change is relatively large, which shows that these two indicators are very important for improving the lighting design level of landscape. Finally, the model is verified by comparing data and model curves. Interactive genetic algorithm is very important to improve the lighting design of landscape, and the optimization model can be widely used in other fields.

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Section: The Basic Research Content Of Interactive Genetic Algorithmmentioning

confidence: 99%

Exploration of Landscape Lighting Design Based on Interactive Genetic Algorithm

Shi

Zheng

2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

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“…KK Rao et'al [22] proposed how to Developing optimal directed random testing modus operandi to diminish interactive faults and lectured the methodical literature and design methodology KK Rao et'al [23] proposed AGA based optimum directed random testing for plummeting interactive faults KK Rao et'al [24] proposed Plummeting interactive fault proneness in software application using GA based optimal directed random testing, KK Rao et'al [25] proposed Software test case generation and it's curtail using G-genetic algorithm…”

Section: Review Of Literaturementioning

confidence: 99%

Prioritization of Test Cases in Software Testing Using M 2 H 2 Optimization

Rao¹,

Rao²,

Chaduvula³

et al. 2022

IJMECS

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By and large, software testing can be well thought-out as a adept technique of achieving improved software quality as well as reliability. On the other hand, the eminence of the test cases had significant effect on the fault enlightening competence of testing activity. Prioritization of Test case (PTC) remnants one challenging issue, as prioritizing test cases remains not up in the direction of abrasion by means of respect to Faults Detected Average Percentage (FDAP) and time execution results. The PTC is predominantly anticipated to scheme assortment of test cases in accomplishing timely optimization by means of preferred properties. Earlier readings have been presented for place in order the accessible test cases in upsurge speed the fault uncovering rate in testing. In this phase, this learning schemes a Modern modified Harris Hawks Optimization centered PTC (M 2 H 2 O-PTC) method for testing. The anticipated M 2 H 2 O-PTC method aims to exhaust the possibilities the FDAP and curtail the complete execution time. Besides, the M 2 H 2 O algorithm is considered for boosting the examination and taking advantage abilities of the conservative H 2 O algorithm. For validating the enhanced efficiency of the M 2 H 2 O-PTC method, an extensive variety of simulations occur on contradictory standard programs and the outcomes are inspected underneath numerous characteristics. The investigational results emphasized enhanced proficiency of the M 2 H 2 O-PTC method in excess of the modern methodologies in standings of dissimilar measures.

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