

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 (<span>SGP-DT</span>) 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, <span>SGP-DT</span>combines the solutions of each run using linear scaling. <span>SGP-DT</span>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 <span>SGP-DT</span>on eight well-known data sets and compare with <span>$\epsilon$-<span style="font-variant:small-caps;">lexicase</span></span>, a state-of-the-art evolutionary technique. <span>SGP-DT</span>achieves small RMSE values, on average 23.19% smaller than the one of <span>$\epsilon$-<span style="font-variant:small-caps;">lexicase</span></span>.

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[^1]: **This is the authors version of this work. It was later published in: European Conference on Genetic Programming (EuroGP '20)**

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