Function Finding Based on Gene Expression Programming

2008 ◽  
Author(s):  
Haifang Mo ◽  
Jiangqing Wang ◽  
Jun Qin ◽  
Lishan Kang
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming ◽  
Function Finding

Population Diversity Strategy in Gene Expression Programming

2011 ◽  
Vol 204-210 ◽  
pp. 288-292 ◽  
Author(s):  
Yong Qiang Zhang ◽  
Jing Xiao
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming ◽  
Evolutionary Process ◽  
Optimal Solution ◽  
Population Diversity ◽  
Population Average ◽  
Population Strategy ◽  
Function Finding ◽  
Diversity Strategy ◽  
Average Fitness

Population diversity is one of the most important factors that influence the convergence speed and evolution efficiency of gene expression programming (GEP) algorithm. In this paper, the population diversity strategy of GEP (GEP-PDS) is presented, inheriting the advantage of superior population producing strategy and various population strategy, to increase population average fitness and decrease generations, to make the population maintain diversification throughout the evolutionary process and avoid “premature” to ensure the convergence ability and evolution efficiency. The simulation experiments show that GEP-PDS can increase the population average fitness by 10% in function finding, and decrease the generations for convergence to the optimal solution by 30% or more compared with other improved GEP.

scholarly journals Component Thermodynamical Selection Based Gene Expression Programming for Function Finding

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Zhaolu Guo ◽  
Zhijian Wu ◽  
Xiaojian Dong ◽  
Kejun Zhang ◽  
Shenwen Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Selective Pressure ◽  
Gene Expression Programming ◽  
Population Diversity ◽  
Minimal Free Energy ◽  
Slow Convergence ◽  
Benchmark Datasets ◽  
Function Finding ◽  
Better Than

Gene expression programming (GEP), improved genetic programming (GP), has become a popular tool for data mining. However, like other evolutionary algorithms, it tends to suffer from premature convergence and slow convergence rate when solving complex problems. In this paper, we propose an enhanced GEP algorithm, called CTSGEP, which is inspired by the principle of minimal free energy in thermodynamics. In CTSGEP, it employs a component thermodynamical selection (CTS) operator to quantitatively keep a balance between the selective pressure and the population diversity during the evolution process. Experiments are conducted on several benchmark datasets from the UCI machine learning repository. The results show that the performance of CTSGEP is better than the conventional GEP and some GEP variations.

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