Created by W.Langdon from gp-bibliography.bib Revision:1.7954
we introduce a multi-objective adaptation of Cartesian Genetic Programming aimed at enhancing image filter performance. We refine the existing Cartesian Genetic Programming framework for image processing by integrating the elite Non-dominated Sorting Genetic Algorithm into the evolutionary process, thus enabling the generation of a set of Pareto front solutions that cater to multiple objectives.
To assess the effectiveness of our framework, we conduct a study using a Urban Traffic dataset and compare our results with those obtained using the standard framework employing a mono-objective evolutionary strategy. Our findings reveal two key advantages of this adaptation. Firstly, it generates individuals with nearly identical precision in one objective while achieving a substantial enhancement in the other objective. Secondly, the use of the Pareto front during the evolution process expands the research space, yielding individuals with improved fitness.",
http://www.evostar.org/2024/ EvoApplications2024 held in conjunction with EuroGP'2024, EvoCOP2024 and EvoMusArt2024",
Genetic Programming entries for Julien Biau Sylvain Cussat-Blanc Herve Luga