An analysis of training models to evolve relocation rules for the container relocation problem

Created by W.Langdon from gp-bibliography.bib Revision:1.8498

@InProceedings{durasevic:2025:GECCOcomp,

• author = "Marko Durasevic and Mateja Dumic and Francisco Javier {Gil Gala} and Domagoj Jakobovic",
• title = "An analysis of training models to evolve relocation rules for the container relocation problem",
• booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion",
• year = "2025",
• editor = "Sarah L. Thomson and Yi Mei",
• pages = "191--194",
• address = "Malaga, Spain",
• series = "GECCO '25 Companion",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, container relocation problem, genetic pogramming, hyper-heuristic, Evolutionary Combinatorial Optimization, Metaheuristics: Poster",
• isbn13 = "979-8-4007-1464-1",
• URL = "https://doi.org/10.1145/3712255.3726780",
• DOI = "doi:10.1145/3712255.3726780",
• size = "4 pages",
• abstract = "The Container Relocation Problem (CRP) is an NP-complete combinatorial optimization problem, indicating that no exact algorithms can solve it within a reasonable time. For this reason, heuristic methods are most commonly used to address this problem. Relocation Rules (RRs) are a simple and fast heuristic applicable in dynamic environments, making them widely used in practice. Designing RRs is a challenging task, which is why this process has recently been automated using Genetic Programming (GP). To develop RRs, GP requires a training set. However, the literature lacks research on which training model and instance types should be used for RR development, even though this choice can significantly impact solution quality. Thus, we proposed and compared several different training models in this study. The results indicate that it is possible to improve results by selecting the right training models, highlighting the need for further research.",
• 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 Marko Durasevic Mateja Dumic Francisco Javier Gil Gala Domagoj Jakobovic

Citations