Surrogate-Assisted Evolution for Efficient Multi-branch Connection Design in Deep Neural Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.8576

@InProceedings{stapleton:2025:GECCOcomp,

• author = "Fergal Stapleton and Daniel {Garcia Nunez} and Yanan Sun and Edgar Galvan",
• title = "Surrogate-Assisted Evolution for Efficient Multi-branch Connection Design in Deep Neural Networks",
• booktitle = "Proceedings of the 2025 Genetic and Evolutionary Computation Conference Companion",
• year = "2025",
• editor = "Bing Xue and Dennis Wilson",
• pages = "747--750",
• 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, neuroevolution, neural architecture search, linear genetic programming, semantics, surrogate modelling: Poster",
• isbn13 = "979-8-4007-1464-1",
• URL = "https://doi.org/10.1145/3712255.3726649",
• DOI = "doi:10.1145/3712255.3726649",
• size = "4 pages",
• abstract = "State-of-the-art Deep Neural Networks (DNNs) often incorporate multi-branch connections, enabling multi-scale feature extraction and enhancing the capture of diverse features. This design improves network capacity and generalisation to unseen data. However, training such DNNs can be computationally expensive. The challenge is further exacerbated by the complexity of identifying optimal network architectures. To address this, we leverage Evolutionary Algorithms (EAs) to automatically discover high-performing architectures, a process commonly known as neuroevolution. We introduce a novel approach based on Linear Genetic Programming (LGP) to encode multi-branch (MB) connections within DNNs, referred to as NeuroLGP-MB. To efficiently design the DNNs, we use surrogate-assisted EAs. While their application in simple artificial neural networks has been influential, we scale their use from dozens or hundreds of sample points to thousands, aligning with the demands of complex DNNs by incorporating a semantic-based approach in our surrogate-assisted EA. Furthermore, we introduce a more advanced surrogate model that outperforms baseline, computationally expensive, and simpler surrogate models.",
• notes = "GECCO-2025 NE A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Fergal Stapleton Daniel Garcia Nunez Yanan Sun Edgar Galvan Lopez

Citations