EGEP: An Event Tracker Enhanced Gene Expression Programming for Data Driven System Engineering Problems

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@Article{journals/tetci/HuangLMDW19,

• title = "{EGEP}: An Event Tracker Enhanced Gene Expression Programming for Data Driven System Engineering Problems",
• author = "Zhengwen Huang and Maozhen Li and Alireza Mousavi and Morad Danishvar and Zidong Wang",
• journal = "IEEE Transactions on Emerging Topics in Computational Intelligence",
• year = "2019",
• number = "2",
• volume = "3",
• pages = "117--126",
• month = apr,
• keywords = "genetic algorithms, genetic programming, gene expression programming, schema theory,event tracker, data driven system engineering, Z-fact0r",
• bibdate = "2020-07-14",
• DOI = "doi:10.1109/TETCI.2018.2864724",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/tetci/tetci3.html#HuangLMDW19",
• size = "10 pages",
• 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 GEP, 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 a GEP schema theory. Experiment results also confirm that EGEP outperforms the GEP with a shorter computation time in an evolution.",

}

Genetic Programming entries for Zhengwen Huang Maozhen Li Alireza Mousavi Morad Danishvar Zidong Wang

Citations