SGP-DT: Semantic Genetic Programming Based on Dynamic Targets

Stefano Ruberto, Valerio Terragni, Jason H. Moore

Abstract: Semantic GP is a promising approach that introduces semantic awareness during genetic evolution. This paper presents a new Semantic GP approach based on Dynamic Target (SGP-DT) that divides the search problem into multiple GP runs. The evolution in each run is guided by a new (dynamic) target based on the residual errors. To obtain the final solution, SGP-DT combines the solutions of each run using linear scaling. SGP-DT presents a new methodology to produce the offspring that does not rely on the classic crossover. The synergy between such a methodology and linear scaling yields to final solutions with low approximation error and computational cost. We evaluate SGP-DT on eight well-known data sets and compare with \(\epsilon \)-lexicase, a state-of-the-art evolutionary technique. SGP-DT achieves small RMSE values, on average 23.19% smaller than the one of \(\epsilon \)-lexicase.