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DC Field | Value | Language |
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dc.contributor.author | Huang, Z | - |
dc.contributor.author | Li, M | - |
dc.contributor.author | Mousavi, A | - |
dc.contributor.author | Danishva, M | - |
dc.contributor.author | Wang, Z | - |
dc.date.accessioned | 2018-07-30T09:25:39Z | - |
dc.date.available | 2018-07-30T09:25:39Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | IEEE Transactions on Emerging Topics in Computational Intelligence, 2019, 3 (2), pp. 117-126 | en_US |
dc.identifier.issn | 2168-6750 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/16654 | - |
dc.description.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. | en_US |
dc.description.sponsorship | European Union's Horizon 2020 research and innovation program; 10.13039/501100012166-National Basic Research Program of China (973 Program); 10.13039/501100003399-Science and Technology Commission of Shanghai Municipality; | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see https://creativecommons.org/licenses/by/3.0/ | - |
dc.rights.uri | https://creativecommons.org/licenses/by/3.0/ | - |
dc.subject | gene expression programming | en_US |
dc.subject | schema theory | en_US |
dc.subject | event tracker | en_US |
dc.subject | data driven system engineering | en_US |
dc.title | EGEP: An Event Tracker Enhanced Gene Expression Programming for Data Driven System Engineering Problems | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1109/TETCI.2018.2864724 | - |
dc.relation.isPartOf | IEEE Transactions on Emerging Topics in Computational Intelligence | - |
pubs.publication-status | Published | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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