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

• URL: http://arxiv.org/abs/2504.11280v1
• Date: Tue, 15 Apr 2025 15:19:42 GMT
• Title: PGU-SGP: A Pheno-Geno Unified Surrogate Genetic Programming For Real-life Container Terminal Truck Scheduling
• Authors: Leshan Tan, Chenwei Jin, Xinan Chen, Rong Qu, Ruibin Bai,
• Abstract summary: This paper proposes a pheno-geno unified surrogate GP algorithm, PGU-SGP, to enhance surrogate sample selection and fitness prediction.<n>With the same training time, PGU-SGP significantly outperforms traditional GP and the state-of-the-art algorithm on most datasets.
• Score: 7.678307721780809
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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% 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.

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