Component Thermodynamical Selection Based Gene Expression Programming for Function Finding

Abstract

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.

A Thermodynamical Selection-Based Discrete Differential Evolution for the 0-1 Knapsack Problem

“…In order to enhance the selection operator of the traditional DE, in this section, we propose an improved selection operator, MFES, which is inspired by the principle of the minimal free energy in thermodynamics, trying to palliate the conflict between the selective pressure and the population diversity to some degree. The principle of the minimal free energy refers to [39][40][41][42], in the annealing process, a metal, beginning with high temperature and chaotic state, is gradually cooled, so that the system at any temperature approximately reaches thermodynamic equilibrium. This cooling process can be regarded as an adaptation procedure to reach the stability of the final crystalline solid.…”

“…According to the principle of the minimal free energy, we can realize that any change from non-equilibrium to equilibrium of the system can be regarded as a consequence of the competition between the mean energy and the entropy, and the temperature T determines their relative weights in the competition [39,41,42]. Accordingly, the competition between the mean energy and the entropy in the annealing process is the same as the competition between the selective pressure and the population diversity of DE during the evolution process.…”