Component Thermodynamical Selection Based Gene Expression Programming for Function Finding

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. 1. Introduction Gene expression programming (GEP) [1, 2], improved genetic programming (GP) with linear representation [3, 4], is an artificial problem solver inspired in natural genotype/phenotype system. GEP combines both the simple, linear string of chromosomes with fixed length to represent the solutions similar to the ones utilized in genetic algorithm (GA) and the ramified structures with different sizes and shapes similar to the parse trees of GP [3, 5, 6]. Thus, GEP has the advantages of both GA and GP, while overcoming some of their individual limitations [3, 4]. Because of its high performance, GEP has attracted increasing attention recently as an efficient and effective data mining approach. Moreover, it has been successfully applied to many fields, such as function finding [7–9], symbolic regression [10–13], parameter optimization [14], rule mining [15], classification [3, 16], time series forecasting [2], prediction of flow number of asphalt mixes [17], prediction of material load [18, 19], prediction of the strength of concrete [20], engineering design [21], and machine scheduling [22, 23]. Although GEP has been successfully employed in a variety of areas, in practical applications, it is found that the conventional GEP usually suffers from premature convergence and slow convergence rate resulting in poor solution quality and/or large computational cost [2–4]. The main reason is that the conventional GEP cannot quantitatively keep a balance between the selective pressure and the population diversity during the evolution process. Therefore, this may lead to trapping in the local optimum and/or slowing down the search speed. In general, increasing selective pressure and promoting population diversity in GEP are often in conflict with each other