Effects of selection schemes in genetic programming for time series prediction

The problem of time series prediction provides a practical benchmark for testing the performance of evolutionary algorithms. In this paper, we compare various selection methods for genetic programming, an evolutionary computation with variable-size tree representations, with application to time series data. Selection is an important operator that controls the dynamics of evolutionary computation. A number of selection operators have been so far proposed and tested in evolutionary algorithms with fixed-size chromosomes. However, the effect of selection schemes remains relatively unexplored in evolutionary algorithms with variable-size representations. We analyze the evolutionary dynamics of genetic programming by means of the selection to response and the selection differential proposed in the breeder genetic algorithm (BGA). The empirical analysis using the laser time-series data suggests that hard selection is more preferable than soft selection. This seems due to the lack of heritability in genetic programming