  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.
