Pretraining Reduces Runtime in Denoising Autoencoder Genetic Programming by an Order of Magnitude

Created by W.Langdon from gp-bibliography.bib Revision:1.7650

@InProceedings{reiter:2023:GECCOcomp,

• author = "Johannes Reiter and Dirk Schweim and David Wittenberg",
• title = "Pretraining Reduces Runtime in Denoising Autoencoder Genetic Programming by an Order of Magnitude",
• booktitle = "GECCO 2023 Student Workshop",
• 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 = "2382--2385",
• 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 computation, pretraining, estimation of distribution genetic programming",
• isbn13 = "9798400701191",
• DOI = "doi:10.1145/3583133.3596332",
• size = "4 pages",
• abstract = "Denoising autoencoder genetic programming (DAE-GP) is an estimation of distribution genetic programming (EDA-GP) algorithm. It uses denoising autoencoder long short-term memory networks as probabilistic model to replace the standard mutation and recombination operators of genetic programming (GP). Recent work has shown several advantages regarding solution length and overall performance of DAE-GP when compared to GP. However, training a neural network at each generation is computationally expensive, where model training is the most time consuming process of DAE-GP. In this work, we propose pretraining to reduce the runtime of the DAE-GP. In pretraining, the neural network is trained preceding the evolutionary search. In experiments on 8 real-world symbolic regression tasks we find that DAE-GP with pretraining has a reduced overall runtime of an order of magnitude while generating individuals with similar or better fitness.",
• 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 Johannes Reiter Dirk Schweim David Wittenberg

Citations