Please use this identifier to cite or link to this item:
Title: EGEP: An Event Tracker Enhanced Gene Expression Programming for Data Driven System Engineering Problems
Authors: Huang, Z
Li, M
Mousavi, A
Danishva, M
Wang, Z
Keywords: Gene Expression Programming;Schema Theory;Event Tracker;Data driven system engineering
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: IEEE Transactions on Emerging Topics in Computational Intelligence
Abstract: Gene expression programming (GEP) is a data driven evolutionary technique that is well suited to correlation mining of system components. With the rapid development of industry 4.0, the number of components in a complex industrial system has increased significantly with a high complexity of correlations. As a result, a major challenge in employing GEP to solve system engineering problems lies in computation efficiency of the evolution process. To address this challenge, this paper presents EGEP, an Event Tracker enhanced Gene Expression Programming which filters irrelevant system components to ensure the evolution process to converge quickly. Furthermore, we introduce three theorems to mathematically validate the effectiveness of EGEP based on Gene expression programming schema theory. Experiment results also confirm that EGEP outperforms Gene expression programming with a shorter computation time in evolution.
ISSN: 2168-6750
Appears in Collections:Dept of Electronic and Computer Engineering Embargoed Research Papers

Files in This Item:
File Description SizeFormat 
Fulltext.pdfFile available on 01/01/2030861.56 kBAdobe PDFView/Open    Request a copy

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.