APPLICATION OF GENE EXPRESSION PROGRAMMING FOR ESTIMATING TOTAL EMISSIVITY OF H2O-CO2 MIXTURES IN AIR-FUEL COMBUSTION WITHOUT SOOT FORMATION

2015 ◽  
Vol 7 (3) ◽  
pp. 191-215
Author(s):  
Babak Maghbooli ◽  
Hamidreza Najafi ◽  
Mohammad Amin Sobati
Keyword(s):  
Gene Expression ◽  
Soot Formation ◽  
Gene Expression Programming ◽  
Fuel Combustion ◽  
Total Emissivity

Gene Expression Programming Evolution Difficulty Prediction Based on Posture Model

2011 ◽  
Vol 22 (5) ◽  
pp. 899-913 ◽  
Author(s):  
Jiao-Ling ZHENG ◽  
Chang-Jie TANG ◽  
Kai-Kuo XU ◽  
Ning YANG ◽  
Lei DUAN ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming

Modeling Self-reported Instrument Data with Gene Expression Programming

2013 ◽  
Vol 45 (6) ◽  
pp. 704-714
Author(s):  
Jinxin QIAN ◽  
Jiayuan YU
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming

Gene expression programming algorithm based on multi-threading evaluator

2013 ◽  
Vol 32 (4) ◽  
pp. 986-989
Author(s):  
Sheng-qiao NI ◽  
Chang-jie TANG ◽  
Ning YANG ◽  
Jie ZUO
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming ◽  
Programming Algorithm

Short-term Load Forecasting by Using Improved GEP and Abnormal Load Recognition

2021 ◽  
Vol 21 (4) ◽  
pp. 1-28
Author(s):  
Song Deng ◽  
Fulin Chen ◽  
Xia Dong ◽  
Guangwei Gao ◽  
Xindong Wu
Keyword(s):  
Gene Expression ◽  
Probability Distribution ◽  
Detection Rate ◽  
Cross Validation ◽  
Normal Load ◽  
Gene Expression Programming ◽  
Load Forecasting ◽  
Short Term ◽  
Forecasting Models ◽  
Short Term Load Forecasting

Load forecasting in short term is very important to economic dispatch and safety assessment of power system. Although existing load forecasting in short-term algorithms have reached required forecast accuracy, most of the forecasting models are black boxes and cannot be constructed to display mathematical models. At the same time, because of the abnormal load caused by the failure of the load data collection device, time synchronization, and malicious tampering, the accuracy of the existing load forecasting models is greatly reduced. To address these problems, this article proposes a Short-Term Load Forecasting algorithm by using Improved Gene Expression Programming and Abnormal Load Recognition (STLF-IGEP_ALR). First, the Recognition algorithm of Abnormal Load based on Probability Distribution and Cross Validation is proposed. By analyzing the probability distribution of rows and columns in load data, and using the probability distribution of rows and columns for cross-validation, misjudgment of normal load in abnormal load data can be better solved. Second, by designing strategies for adaptive generation of population parameters, individual evolution of populations and dynamic adjustment of genetic operation probability, an Improved Gene Expression Programming based on Evolutionary Parameter Optimization is proposed. Finally, the experimental results on two real load datasets and one open load dataset show that compared with the existing abnormal data detection algorithms, the algorithm proposed in this article have higher advantages in missing detection rate, false detection rate and precision rate, and STLF-IGEP_ALR is superior to other short-term load forecasting algorithms in terms of the convergence speed, MAE, MAPE, RSME, and R 2 .

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