Article: Knowledge-based genetic algorithm approach to optimise gated recurrent unit for semantic web service classification Journal: International Journal of Intelligent Systems Technologies and Applications (IJISTA) 2023 Vol.21 No.4 pp.366 - 385 Abstract: The expeditious proliferation of web services over the current web-cyberspace becomes a significant challenge for the web service classification. Recent reports suggest a deep learning algorithm namely, gated recurrent unit, performs well. But the performance of the GRU classifier hinges on structure of the GRU classifier namely, hidden neural network (HNN) and training data. Hence, it is significant to predict optimal HNN and training dataset. The recent studies revealed that genetic algorithm (GA) is most suited for this optimisation problem. However, GA is inefficient in terms of convergence rate and generation of prudent solution. For that, domain knowledge is incorporated in GA operator in order to accelerate convergence rate called as knowledge-based genetic algorithm (KBGA). The research work adopts KBGA to optimise the structure of GRU, so that the classifier outperforms the classification process. Extensive analysis and statistical hypothesis test have been done to show the efficacy of KBGA. Inderscience Publishers - linking academia, business and industry through research

Title: Knowledge-based genetic algorithm approach to optimise gated recurrent unit for semantic web service classification

Authors: S. Sridevi; G.R. Karpagam; B. Vinoth Kumar

Addresses: School of Computer Science and Engineering, VIT University, Vellore, 632014, Tamilnadu, India ' Department of Computer Science and Engineering, PSG College of Technology, Peelamedu, Coimbatore, 641004, Tamilnadu, India ' Department of Information Technology, PSG College of Technology, Peelamedu, Coimbatore, 641004, Tamilnadu, India

Abstract: The expeditious proliferation of web services over the current web-cyberspace becomes a significant challenge for the web service classification. Recent reports suggest a deep learning algorithm namely, gated recurrent unit, performs well. But the performance of the GRU classifier hinges on structure of the GRU classifier namely, hidden neural network (HNN) and training data. Hence, it is significant to predict optimal HNN and training dataset. The recent studies revealed that genetic algorithm (GA) is most suited for this optimisation problem. However, GA is inefficient in terms of convergence rate and generation of prudent solution. For that, domain knowledge is incorporated in GA operator in order to accelerate convergence rate called as knowledge-based genetic algorithm (KBGA). The research work adopts KBGA to optimise the structure of GRU, so that the classifier outperforms the classification process. Extensive analysis and statistical hypothesis test have been done to show the efficacy of KBGA.

Keywords: web service classification; semantics; sparse features; meta-heuristic search; genetic algorithm; GA; knowledge-based genetic algorithm; KBGA; statistical hypothesis test.

DOI: 10.1504/IJISTA.2023.134991

International Journal of Intelligent Systems Technologies and Applications, 2023 Vol.21 No.4, pp.366 - 385

Received: 18 Oct 2022
Accepted: 27 Jul 2023
Published online: 23 Nov 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Knowledge-based genetic algorithm approach to optimise gated recurrent unit for semantic web service classification

Keep up-to-date