- author = "Pedro Carvalho and Nuno Lourenco and Filipe Assuncao and Penousal Machado",
- title = "{AutoLR}: An Evolutionary Approach to Learning Rate Policies",
- year = "2020",
- editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
- booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference",
- pages = "672--680",
- address = "internet",
- series = "GECCO '20",
- month = jul # " 8-12",
- organisation = "SIGEVO",
- keywords = "genetic algorithms, genetic programming, grammatical evolution, structured grammatical evolution, learning rate schedulers",
- isbn13 = "9781450371285",
-
URL = "
http://www.human-competitive.org/sites/default/files/carvalho-autolr.txt",
-
URL = "
http://www.human-competitive.org/sites/default/files/autolr_-_an_evolutionary_approach_to_learning_rate_policies.pdf",
-
URL = "https://doi.org/10.1145/3377930.3390158",
-
DOI = "
doi:10.1145/3377930.3390158",
- size = "9 pages",
- abstract = "The choice of a proper learning rate is paramount for good Artificial Neural Network training and performance. In the past, one had to rely on experience and trial-and-error to find an adequate learning rate. Presently, a plethora of state of the art automatic methods exist that make the search for a good learning rate easier. While these techniques are effective and have yielded good results over the years, they are general solutions. This means the optimization of learning rate for specific network topologies remains largely unexplored. This work presents AutoLR, a framework that evolves Learning Rate Schedulers for a specific Neural Network Architecture using Structured Grammatical Evolution. The system was used to evolve learning rate policies that were compared with a commonly used baseline value for learning rate. Results show that training performed using certain evolved policies is more efficient than the established baseline and suggest that this approach is a viable means of improving a neural network's performance.",
-
notes = "Entered 2021 HUMIES
Also known as \cite{10.1145/3377930.3390158} \cite{DBLP:conf/gecco/Carvalho0AM20}
GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)",
