- author = "Stefano Ruberto and Valerio Terragni and Jason H. Moore",
- title = "{SGP-DT}: Semantic Genetic Programming Based on Dynamic Targets",
- booktitle = "EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming",
- year = "2020",
- month = "15-17 " # apr,
- editor = "Ting Hu and Nuno Lourenco and Eric Medvet",
- series = "LNCS",
- volume = "12101",
- publisher = "Springer Verlag",
- address = "Seville, Spain",
- pages = "167--183",
- organisation = "EvoStar, Species",
- keywords = "genetic algorithms, genetic programming, Semantic GP, Natural selection, Symbolic Regression, Residuals, Linear scaling, Crossover, Mutation",
- isbn13 = "978-3-030-44093-0",
-
URL = "
https://valerio65.github.io/assets/pdf/ruberto-eurogp-2020.pdf",
-
DOI = "
doi:10.1007/978-3-030-44094-7_11",
-
video_url = "
https://www.youtube.com/watch?v=xOz8BVqsHGY",
- size = "16 pages",
- 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 e-lexicase, a state-of-the-art evolutionary technique. SGP-DT achieves small RMSE values, on average 23.19percent smaller than the one of epsilon-lexicase.",
-
notes = "Nominated for best paper.
Also known as \cite{ruberto-eurogp-2020} Slides: https://valerio65.github.io/assets/pdf/ruberto-eurogp-2020-slides.pdf
http://www.evostar.org/2020/cfp_eurogp.php Part of \cite{Hu:2020:GP} EuroGP'2020 held in conjunction with EvoCOP2020, EvoMusArt2020 and EvoApplications2020",
