Evolutionary Algorithms for Segment Optimization in Vectorial GP

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{fleck:2023:GECCOcomp,

• author = "Philipp Fleck and Stephan Winkler and Michael Kommenda and Sara Silva and Leonardo Vanneschi and Michael Affenzeller",
• title = "Evolutionary Algorithms for Segment Optimization in Vectorial {GP}",
• booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference",
• year = "2023",
• editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
• pages = "439--442",
• address = "Lisbon, Portugal",
• series = "GECCO '23",
• month = "15-19 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, evolutionary algorithms, vectorial, symbolic regression: Poster",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583133.3590668",
• size = "4 pages",
• abstract = "Vectorial Genetic Programming (Vec-GP) extends regular GP by allowing vectorial input features (e.g. time series data), while retaining the expressiveness and interpretability of regular GP. The availability of raw vectorial data during training, not only enables Vec-GP to select appropriate aggregation functions itself, but also allows Vec-GP to extract segments from vectors prior to aggregation (like windows for time series data). This is a critical factor in many machine learning applications, as vectors can be very long and only small segments may be relevant. However, allowing aggregation over segments within GP models makes the training more complicated. We explore the use of common evolutionary algorithms to help GP identify appropriate segments, which we analyze using a simplified problem that focuses on optimizing aggregation segments on fixed data. Since the studied algorithms are to be used in GP for local optimization (e.g. as mutation operator), we evaluate not only the quality of the solutions, but also take into account the convergence speed and anytime performance. Among the evaluated algorithms, CMA-ES, PSO and ALPS show the most promising results, which would be prime candidates for evaluation within GP.",
• notes = "GECCO-2023 A Recombination of the 32nd International Conference on Genetic Algorithms (ICGA) and the 28th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Philipp Fleck Stephan M Winkler Michael Kommenda Sara Silva Leonardo Vanneschi Michael Affenzeller

Citations