Model-Adaptive Reference Points Generation based on Gaussian Process for Many-Objective Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.8519

@InProceedings{masood:2025:GECCOcomp,

• author = "Atiya Masood",
• title = "Model-Adaptive Reference Points Generation based on Gaussian Process for Many-Objective Optimization",
• booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion",
• year = "2025",
• editor = "Arnaud Liefooghe and Tapabrata Ray",
• pages = "395--398",
• 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, many-objective optimization, adaptive reference points, gaussian process model, evolutionary algorithm, Evolutionary Multiobjective Optimization: Poster",
• isbn13 = "979-8-4007-1464-1",
• URL = "https://doi.org/10.1145/3712255.3726586",
• DOI = "doi:10.1145/3712255.3726586",
• size = "4 pages",
• abstract = "Many-objective optimization problems (MaOPs) present unique challenges, particularly in achieving both convergence and diversity across high-dimensional Pareto fronts. These challenges become even more pronounced when the Pareto fronts are irregular or disconnected. Traditional methods that rely on uniformly distributed reference points often struggle to perform well under such conditions. This paper introduces a new algorithm called Model Adaptive Reference Points NSGA-III (MARP-NSGA-III) to address these issues. The algorithm uses Gaussian Processes (GPs) to generate reference points based on the dynamic distribution of solutions. By incorporating NSGA-III, MARP-NSGA-III models the evolving Pareto front and adaptively allocates reference points, ensuring they align more closely with the front's structure. This study provides a detailed framework explaining the core components of the method, including how the GP model is constructed, how areas of interest are estimated, and how reference points are generated. MARP-NSGA-III represents a significant step forward in solving 11 MaOPs, offering a flexible and adaptive approach. Our experiments show that, for benchmark problems, our proposed algorithm (MARP-NSGAIII) performs better than other many-objective competing algorithms. Gaussian Process-Driven Adaptive Reference Point Generation for Many-Objective Optimization.",
• notes = "GECCO-2025 EMO A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Atiya Masood

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