Automatic Generation and Optimization of Test case using Hybrid Cuckoo Search and Bee Colony Algorithm

Lakshminarayana, P; Sureshkumar, T.

doi:10.1515/jisys-2019-0051

Cited by 28 publications

(19 citation statements)

References 30 publications

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“…MLP is a network structure commonly used in artificial neural network (ANN), which is a multilayer composite of perceptrons consisting of input layer, hidden layer, and output layer. Besides the input layer, each node of the other layers is a neuron with a nonlinear activation function [35,36]. The MLP network learning process consists of building a feature vector and passing it to the hidden layer.…”

Section: Methods (Machine Learning Models and Hyperparameter Optimizamentioning

confidence: 99%

A Comparative Study of Machine Learning Models with Hyperparameter Optimization Algorithm for Mapping Mineral Prospectivity

et al. 2021

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Selecting internal hyperparameters, which can be set by the automatic search algorithm, is important to improve the generalization performance of machine learning models. In this study, the geological, remote sensing and geochemical data of the Lalingzaohuo area in Qinghai province were researched. A multi-source metallogenic information spatial data set was constructed by calculating the Youden index for selecting potential evidence layers. The model for mapping mineral prospectivity of the study area was established by combining two swarm intelligence optimization algorithms, namely the bat algorithm (BA) and the firefly algorithm (FA), with different machine learning models. The receiver operating characteristic (ROC) and prediction-area (P-A) curves were used for performance evaluation and showed that the two algorithms had an obvious optimization effect. The BA and FA differentiated in improving multilayer perceptron (MLP), AdaBoost and one-class support vector machine (OCSVM) models; thus, there was no optimization algorithm that was consistently superior to the other. However, the accuracy of the machine learning models was significantly enhanced after optimizing the hyperparameters. The area under curve (AUC) values of the ROC curve of the optimized machine learning models were all higher than 0.8, indicating that the hyperparameter optimization calculation was effective. In terms of individual model improvement, the accuracy of the FA-AdaBoost model was improved the most significantly, with the AUC value increasing from 0.8173 to 0.9597 and the prediction/area (P/A) value increasing from 3.156 to 10.765, where the mineral targets predicted by the model occupied 8.63% of the study area and contained 92.86% of the known mineral deposits. The targets predicted by the improved machine learning models are consistent with the metallogenic geological characteristics, indicating that the swarm intelligence optimization algorithm combined with the machine learning model is an efficient method for mineral prospectivity mapping.

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Section: Methods (Machine Learning Models and Hyperparameter Optimizamentioning

confidence: 99%

A Comparative Study of Machine Learning Models with Hyperparameter Optimization Algorithm for Mapping Mineral Prospectivity

et al. 2021

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“…Some researchers compared and assessed the applications of various metaheuristics techniques for optimization of testing process and argued that swarm-based techniques perform better for the given scenario [15]. Various evolutionary techniques are used for path testing for achieving higher coverage [16]. Omur Sahin et.…”

Section: Related Workmentioning

confidence: 99%

Hybrid swarm intelligence-based software testing techniques for improving quality of component based software

Palak

Gulia

Gill

2021

IJEECS

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Being a time-consuming and costly activity, software testing always demands optimization and automation. Software testing is an important activity to achieve quality and customer satisfaction. This paper presents a comparative evaluation of different hybrid automated software testing techniques using the concepts of soft computing for overall quality enhancement. A comparison between three hybrid automation techniques is carried out i.e., hybrid ant colony optimization-genetic algorithms (ACO-GA), hybrid artificial bee colony (ABC)-Naïve Bayes, hybrid ABC-GA along with three parent approaches. The comparison is made by applying these hybrid techniques for the selection of minimized test suites thus reducing overall testing effort and eliminating useless or redundant test cases. The experimental results prove the efficiency of these hybrid approaches in different scenarios. The impact of automated testing techniques for quality enhancement is assessed in terms of defect density and defect detection percentage.

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“…Test case development is a tool for defining test information or following the requirements for application development. The development of test cases is a critical term being included in software development, and could be extracted from the specifications of application specifications [41]. An automated test scenario methodology immediately decides where the check proposed methodology produces the data sets and test results [42].…”

Section: Test Case: Optimization and Automatic Generationmentioning

confidence: 99%

“…In this cuckoo analysis process, the test scenario is provided as an entry to the composite process to evaluate all test scenarios received. In the cuckoo hunt, the test scenario is represented as the native species and an ideal test scenario is sought utilizing the Levy method [41].…”

Section: Test Case: Optimization and Automatic Generationmentioning

confidence: 99%

Bio-Inspired Computing-A Dive into Critical Problems, Potential Architecture and Techniques

et al. 2021

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The integration and development of electronics in the recent years have impacted a major development on the world and humans, one among that is nanotechnology. Nanotechnology has achieved a greater progress in biomedical engineering in diagnosis and treatment, leading to the introduction of nanomaterials for drug delivery, prostheses and implanting. This work describes the Bio-Nano-tools that are developed based on iron oxide properties, automated tools used in the tumor detection, satin bowerbird optimization (SBO) technique employed in diagnosis of skin cancer. This work also highlights the post introduction development of nanomaterials like combination of nanotechnology with Artificial Intelligence (AI) and its impact, advancement of nanomaterials based on their operations, shapes and characteristics that leading to the growth of nanostructures with operations control properties. The paper also highlights the improvement of silicon neuromorphic photonic processors and parallel simulators in the development of bio inspired computing. We are hopeful that this review article provides future directions in Bio-Inspired Computing. HIGHLIGHTS In processing of medical images, noise plays a challenging role. So, reduction of noise is important, with the data that is analyzed in our review, it is shown that noise reduction can be achieved using Gradient and Feature Adaptive Contour (GFAC) model, with effective results There are many algorithms that are used for skin cancer detection, as highlighted in our review. Amongst all the methods, the particle swarm optimization (PSO) algorithm shows impressive results when compared to other models in terms of feature extraction in dermoscopy images Satin bowerbird optimization (SBO) algorithm helps in improving the CNN efficiency. The optimal justification of the hyper parameter numbers in convolutional neural network (CNN) for skin cancer diagnosis can be achieved using an SBO algorithm

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Automatic Generation and Optimization of Test case using Hybrid Cuckoo Search and Bee Colony Algorithm

Cited by 28 publications

References 30 publications

A Comparative Study of Machine Learning Models with Hyperparameter Optimization Algorithm for Mapping Mineral Prospectivity

A Comparative Study of Machine Learning Models with Hyperparameter Optimization Algorithm for Mapping Mineral Prospectivity

Hybrid swarm intelligence-based software testing techniques for improving quality of component based software

Bio-Inspired Computing-A Dive into Critical Problems, Potential Architecture and Techniques

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