Genetic Evolutionary Learning Fitness Function (GELFF) for Feature Diagnosis to Software Fault Prediction
P. Patchaiammal1, R. Thirumalaiselvi2

1P. Patchaiammal, Research Scholar, Bharath University, Chennai India. 

2R. Thirumalaiselvi, Research Supervisor, Bharath University, Chennai, Assistant Professor, Computer Science Department, Govt. Arts College (Men), Nandanam, Chennai India. 

Manuscript received on 15 September 2019 | Revised Manuscript received on 23 September 2019 | Manuscript Published on 11 October 2019 | PP: 1151-1161 | Volume-8 Issue-11S September 2019 | Retrieval Number: K123309811S19/2019©BEIESP | DOI: 10.35940/ijitee.K1233.09811S19

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Abstract: Nowadays, proper feature selection f+orFault prediction is very perplexing task. Improper feature selection may lead to bad result. To avoid this, there is a need to find the aridity of software fault. This is achieved by finding the fitness of the evolutionaryAlgorithmic function. In this paper, we finalize the Genetic evolutionarynature of our Feature set with the help of Fitness Function. Feature Selection is the objective of the prediction model tocreate the underlying process of generalized data. The wide range of data like fault dataset, need the better objective function is obtained by feature selection, ranking, elimination and construction. In this paper, we focus on finding the fitness of the machine learning function which is used in the diagnostics of fault in the software for the better classification.

Keywords: Fitness Function, Parents, Chromosomes, Crossover, Mutations, Fault Diagnostics, Genetic evolutionary Programming (GEP), Machine Learning (ML), Feature Selection, Feature extraction,Genetic evolutionary Learning Fitness Function (GELFF).
Scope of the Article: Deep Learning