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View all- Guevara IMargaria T(2023)MazeGen: A Low-Code Framework for Bootstrapping Robotic Navigation Scenarios for Smart Manufacturing ContextsElectronics10.3390/electronics1209205812:9(2058)Online publication date: 29-Apr-2023
General controllers evolved through grammatical evolution with a divergent search
Authors: 
Enrique Naredo, Conor Ryan, Ivan Guevara, Tiziana Margaria, Paulo Urbano, 
Leonardo TrujilloAuthors Info & Claims
Pages 243 - 244
Abstract
In this work, we analyse the performance of Novelty Search (NS) in a set of generalization experiments in a navigation task with Grammatical Evolution. Agents are trained on a single, simple environment, and tested on a selection of related, increasingly more difficult environments. We show that agents discovered with NS, although using a tiny number (six) of training samples, successfully generalise to these more difficult environments.
References
[1]
Loukas Georgiou. [n.d.]. jGE NetLogo v2.0. https://web.archive.org/web/20101129085227/http://www.bangor.ac.uk/~eep201/jge/ Last Modified: 22 May 2010.
[2]
Loukas Georgiou and William J. Teahan. 2006. jGE - A Java implementation of Grammatical Evolution. In 10th WSEAS International Conference on Systems. Athens, Greece, 534--869.
[3]
Loukas Georgiou and William J. Teahan. 2010. Grammatical Evolution and the Santa Fe Trail Problem. In International Conference on Evolutionary Computation (ICEC). SciTePress, Valencia, Spain, 10--19.
[4]
Joel Lehman and Kenneth O. Stanley. 2010. Efficiently evolving programs through the search for novelty. In GECCO, Martin Pelikan and Jürgen Branke (Eds.). ACM, 837--844.
[5]
E. Naredo, P. Urbano, and L. Trujillo. 2017. The training set and generalization in grammatical evolution for autonomous agent navigation. Soft Comput 21, 15 (2017), 4399--4416.
[6]
Conor Ryan, J. J. Collins, and Michael O'Neill. 1998. Grammatical Evolution: Evolving Programs for an Arbitrary Language. In EuroGP (Lecture Notes in Computer Science), Wolfgang Banzhaf, Riccardo Poli, Marc Schoenauer, and Terence C. Fogarty (Eds.), Vol. 1391. Springer, 83--96.
[7]
U. Wilensky. 1999. NetLogo 4.1. https://ccl.northwestern.edu/netlogo/references.shtml/ Accessed: Jan 01, 2019.
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- General controllers evolved through grammatical evolution with a divergent search
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Published: 08 July 2020
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- Fundação para a Ciência e a Tecnologia
- Science Foundation Ireland
- Consejo Nacional de Ciencia y Tecnología
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GECCO '20
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View all- Guevara IMargaria T(2023)MazeGen: A Low-Code Framework for Bootstrapping Robotic Navigation Scenarios for Smart Manufacturing ContextsElectronics10.3390/electronics1209205812:9(2058)Online publication date: 29-Apr-2023
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