Automatic Design of Specialized Variation Operators for the Multi-Objective Quadratic Assignment Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.8528

@InProceedings{morales-paredes:2025:GECCO2,

• author = "Adrian Isai Morales-Paredes and Julio Juarez and Jesus Guillermo Falcon-Cardona and Hugo Terashima-Marin and Carlos {Coello Coello}",
• title = "Automatic Design of Specialized Variation Operators for the Multi-Objective Quadratic Assignment Problem",
• booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference",
• year = "2025",
• editor = "Marie-Eleonore Kessaci and Anna V. Kononova",
• pages = "1153--1161",
• 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, grammatical evolution, Learning for Evolutionary Computation",
• isbn13 = "979-8-4007-1465-8",
• URL = "https://doi.org/10.1145/3712256.3726456",
• DOI = "doi:10.1145/3712256.3726456",
• size = "9 pages",
• abstract = "The development of specialized, domain-specific operators has significantly enhanced the performance of evolutionary algorithms for solving optimization problems. However, creating such operators often requires substantial effort from human experts, making the process slow, resource-intensive, and heavily reliant on domain knowledge. To overcome these limitations, generation hyper-heuristics provide a framework for automating the design of variation operators by evolving combinations of heuristic components without direct expert input. In this work, we propose a generation hyper-heuristic method based on grammatical evolution to automatically design variation operators (crossover and mutation) tailored to the multi-objective quadratic assignment problem (mQAP)-a challenging combinatorial optimization problem with many real-world applications. Using the proposed method, variation operators were generated considering six mQAP instances with two and three objectives, leveraging MOEA/D as a multi-objective optimizer. For validation, the generated operators were evaluated on unseen instances. Our experimental results indicate that the evolved operators enhance the performance of MOEA/D compared to standard crossover operators. Furthermore, the top-performing operator in training did not always generalize best to larger instances, while some lower-ranked operators showed better adaptability. These results highlight the potential of automated operator design in effectively tackling complex optimization problems like the mQAP.",
• notes = "GECCO-2025 L4EC A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Adrian Isai Morales-Paredes Julio Juarez Jesus Guillermo Falcon-Cardona Hugo Terashima-Marin Carlos Artemio Coello Coello

Citations