GPLAB – A Genetic Programming Toolbox for MATLAB
Sara Silva,
FCT, Universidade de Coimbra –
Abstract:
Genetic Programming (GP) is the automated learning of computer programs. Basically a search process, it is capable of solving complex problems by evolving populations of computer programs, using Darwinian evolution and Mendelian genetics as inspiration.
GPLAB is a Genetic Programming toolbox for MATLAB. Besides most of the traditional functionalities used in GP, it also implements two additional features: (1) a method for automatically adapting the genetic operator probabilities in runtime, allowing the use of the toolbox as a test bench for new genetic operators; (2) several of the best state-of-the-art techniques for controlling the well known bloat problem, including some that result in automatic resizing of the population in runtime to save computational resources. Combining a highly modular and adaptable structure with the concern for automatic setting of most parameters, GPLAB suits all kinds of users, from the layman who wants to use it as a ‘black box’, to the advanced researcher who intends to build and test new functionalities. The toolbox and its documentation are freely available for download at http://gplab.sourceforge.net. The latest version ensures minimal compatibility with Octave.
Date: 2007-May-24 Time: 16:30:00 Room: 336
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Keynote speakers:
David Posada, University of Vigo (class)
João Alves, University of Vigo (hands-on)
Nadia El-Mabrouk, Université de Montréal (class)
Mattéo Delabre, Université de Montréal (hands-on)
Ran Libeskind-Hadas, Claremont McKenna College (class and hands-on)
Russell Schwartz, Carnegie Mellon University (class and hands-on)
See the preliminary agenda at: https://olissipo.inesc-id.pt/tree-tango-school
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The registration fee is 250€ for students and OLISSIPO members and 350€ for postdocs or other researchers (meals indicated at the schedule of the school are included, accommodation and flights are not). All details will be made available upon registration.
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Check online for information about past editions: LxMLS 2011, LxMLS 2012, LxMLS 2013, LxMLS 2014, LxMLS 2015, LxMLS 2016, LxMLS 2017, LxMLS 2018, LxMLS 2019, LxMLS 2020, LxMLS 2021, LxMLS 2022 (you can also watch the videos of the lectures for 2016, 2017, 2018, and 2020).
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