Schema theory-based data engineering in gene expression programming for big data analytics
- Submitting institution
-
The University of West London
- Unit of assessment
- 12 - Engineering
- Output identifier
- 12003
- Type
- D - Journal article
- DOI
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10.1109/TEVC.2017.2771445
- Title of journal
- IEEE Transactions on Evolutionary Computation
- Article number
- -
- First page
- 792
- Volume
- 22
- Issue
- 5
- ISSN
- 1089-778X
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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4
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The methods and computational power required to retrieve information from Big Data (Data Mining) is one of the major problems in computing today. This work fills the research gap related to the structure and the size of input data, when parallel computation techniques are applied, by proposing three novel amendments that significantly improve the performance of Gene Expression Programming (GEN) technique and accelerates the Correlation Mining process. This is an original research published in a high impact journal that significantly contribute to the evolution of computing techniques within the concept of Big Data analysis.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -