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.
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Notes
- 1.
\(f(x)=10/(5 + \sum _{i=1}^{5} (x_i -3)^2)\).
- 2.
- 3.
calculated with \(((M_T- M_D)/M_T) \cdot 100\), where \(M_D\) is the median RMSE of SGP-DT and \(M_T\) is the one of the competing technique.
- 4.
for readability reasons we omitted 4 out-layers for lasso, 13 for \(\epsilon \)-lexicase, 30 for SGP-DT, 30 for DT-NM and 35 for DT-EM.
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Ruberto, S., Terragni, V., Moore, J.H. (2020). SGP-DT: Semantic Genetic Programming Based on Dynamic Targets. In: Hu, T., Lourenço, N., Medvet, E., Divina, F. (eds) Genetic Programming. EuroGP 2020. Lecture Notes in Computer Science(), vol 12101. Springer, Cham. https://doi.org/10.1007/978-3-030-44094-7_11
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