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Semantic GP is a promising approach that introduces semantic awareness duringgenetic evolution. This paper presents a new Semantic GP approach based onDynamic 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 theresidual errors. To obtain the final solution, SGP-DT combines the solutions ofeach run using linear scaling. SGP-DT presents a new methodology to produce theoffspring that does not rely on the classic crossover. The synergy between sucha methodology and linear scaling yields to final solutions with lowapproximation error and computational cost. We evaluate SGP-DT on eightwell-known data sets and compare with {\epsilon}-lexicase, a state-of-the-artevolutionary technique. SGP-DT achieves small RMSE values, on average 23.19%smaller than the one of {\epsilon}-lexicase.

SGP-DT: Semantic Genetic Programming Based on Dynamic Targets