PGU-SGP: A Pheno-Geno Unified Surrogate Genetic Programming For Real-life Container Terminal Truck Scheduling

Created by W.Langdon from gp-bibliography.bib Revision:1.8464

@InProceedings{tan:2025:GECCO,

• author = "Leshan Tan and Chenwei Jin and Xinan Chen and Rong Qu and Ruibin Bai",
• title = "{PGU-SGP:} A Pheno-Geno Unified Surrogate Genetic Programming For Real-life Container Terminal Truck Scheduling",
• booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference",
• year = "2025",
• editor = "Sarah L. Thomson and Yi Mei",
• pages = "322--330",
• address = "Malaga, Spain",
• series = "GECCO '25",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, Evolutionary Combinatorial Optimization, Metaheuristics",
• isbn13 = "979-8-4007-1465-8",
• URL = "https://doi.org/10.1145/3712256.3726326",
• DOI = "doi:10.1145/3712256.3726326",
• size = "9 pages",
• abstract = "Data-driven genetic programming (GP) has proven highly effective in solving combinatorial optimization problems under dynamic and uncertain environments. A central challenge lies in fast fitness evaluations on large training datasets, especially for complex real-world problems involving time-consuming simulations. Surrogate models, like phenotypic characterization (PC)-based K-nearest neighbors (KNN), have been applied to reduce computational cost. However, the PC-based similarity measure is confined to behavioral characteristics, overlooking genotypic differences, which can limit surrogate quality and impair performance. To address these issues, this paper proposes a pheno-geno unified surrogate GP algorithm, PGU-SGP, integrating phenotypic and genotypic characterization (GC) to enhance surrogate sample selection and fitness prediction. A novel unified similarity metric combining PC and GC distances is proposed, along with an effective and efficient GC representation. Experimental results of a real-life vehicle scheduling problem demonstrate that PGU-SGP reduces training time by approximately 76 percent while achieving comparable performance to traditional GP. With the same training time, PGU-SGP significantly outperforms traditional GP and the state-of-the-art algorithm on most datasets. Additionally, PGU-SGP shows faster convergence and improved surrogate quality by maintaining accurate fitness rankings and appropriate selection pressure, further validating its effectiveness.",
• notes = "GECCO-2025 ECOM A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Leshan Tan Chenwei Jin Xinan Chen Rong Qu Ruibin Bai

Citations