Unveiling the dynamics of NOx pollution in internal combustion engines by Structured Grammatical Evolution

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

@InProceedings{llamazares-lopez:2025:GECCO,

• author = "Marcos {Llamazares Lopez} and Daniel Parra and Jose Manuel {Velasco Cabo} and Oscar Garnica and Rafael Jacinto {Villanueva Mico} and J. Ignacio Hidalgo",
• title = "Unveiling the dynamics of {NOx} pollution in internal combustion engines by Structured Grammatical Evolution",
• booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference",
• year = "2025",
• editor = "Roman Kalkreuth and Alexander Brownlee",
• pages = "1388--1396",
• 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, Real World Applications",
• isbn13 = "979-8-4007-1465-8",
• URL = "https://doi.org/10.1145/3712256.3726421",
• DOI = "doi:10.1145/3712256.3726421",
• size = "9 pages",
• abstract = "The formation of nitrogen oxides (NOx) in combustion systems is notable for its harmful impact on public health and the environment. Therefore, it is imperative to develop models to predict NOx formation in different situations. These models are designed to capture the characteristics of three distinct engine operating states: nominal, startup, and saturation. The nominal state represents the typical operating conditions, the startup state refers to the initial phase of the operation of the engine, and the saturation state corresponds to the operation of the engine at its maximum capacity. We applied dynamic structured grammatical evolution to obtain a set of interpretable expressions, which are mathematical representations capable of capturing the dynamics of NOx formation in combustion systems and that can be easily interpreted. These models were compared with traditional differential equation-based models to assess their interpretability and predictive accuracy for the three scenarios. Through our approach, we obtained a set of interpretable expressions that improved those obtained by a differential equation-based mathematical model, providing a more transparent and intuitive understanding of the system's behavior. Our technique seeks to unveil the dynamics of NOx formation processes that could significantly reduce NOx emissions and mitigate their impact on global environmental pollution.",
• notes = "GECCO-2025 RWA A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Marcos Llamazares Lopez Daniel Parra Rodriguez Jose Manuel Velasco Cabo Oscar Garnica Rafael Jacinto Villanueva Mico Jose Ignacio Hidalgo Perez

Citations