A semantic genetic programming framework based on dynamic targets

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Ruberto:GPEM,

• author = "Stefano Ruberto and Valerio Terragni and Jason H. Moore",
• title = "A semantic genetic programming framework based on dynamic targets",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2021",
• volume = "22",
• number = "4",
• pages = "463--493",
• month = dec,
• note = "Special Issue: Highlights of Genetic Programming 2020 Events",
• keywords = "genetic algorithms, genetic programming, Semantic GP, Genetic Programming, Natural Selection, Symbolic Regression, Residuals, Linear Scaling, Crossover, Mutation",
• ISSN = "1389-2576",
• URL = "https://rdcu.be/cy6pf",
• DOI = "doi:10.1007/s10710-021-09419-3",
• size = "31 pages",
• abstract = "Semantic GP is a promising branch of GP that introduces semantic awareness during genetic evolution to improve various aspects of GP. 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 of previous runs. 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 final solutions with low approximation error and computational cost. We evaluate SGP-DT on eleven well-known data sets and compare with e-lexicase, a state-of-the-art evolutionary technique, and seven Machine Learning techniques. SGP-DT achieves small RMSE values, on average 23.19percent smaller than the one of epsilon-lexicase. Tuning SGP-DT configuration greatly reduces the computational cost while still obtaining competitive results.",

}

Genetic Programming entries for Stefano Ruberto Valerio Terragni Jason H Moore

Citations