Benchmark symbolic regression

Published in Matlab

Script for data generation

Script to create training and testing data for benchmark symbolic regression.

Based on

  1. James McDermott, David R White, Sean Luke, Luca Manzoni, Mauro Castelli, Leonardo Vanneschi, Wojciech Jaskowski, Krzysztof Krawiec, Robin Harper, Kenneth De Jong and Una-May O'Reilly. Genetic programming needs better benchmarks. In Terry Soule, Anne Auger, Jason Moore, David Pelta, Christine Solnon, Mike Preuss, Alan Dorin, Yew-Soon Ong, Christian Blum, Dario Landa Silva, Frank Neumann, Tina Yu, Aniko Ekart, Will Browne, Tim Kovacs, Man-Leung Wong, Clara Pizzuti, Jon Rowe, Tobias Friedrich, Giovanni Squillero, Nicolas Bredeche, Stephen Smith, Alison Motsinger-Reif, Jose Lozano, Martin Pelikan, Silja Meyer-Nienberg, Christian Igel, Greg Hornby, Rene Doursat, Steve Gustafson, Gustavo Olague, Shin Yoo, John Clark, Gabriela Ochoa, Gisele Pappa, Fernando Lobo, Daniel Tauritz, Jurgen Branke and Kalyanmoy Deb (eds.). GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference. 2012, 791–798. DOI BibTeX

    	author = "James McDermott and David R. White and Sean Luke and Luca Manzoni and Mauro Castelli and Leonardo Vanneschi and Wojciech Jaskowski and Krzysztof Krawiec and Robin Harper and Kenneth {De Jong} and Una-May O'Reilly",
    	title = "Genetic programming needs better benchmarks",
    	booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference",
    	year = 2012,
    	editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb",
    	isbn13 = "978-1-4503-1177-9",
    	pages = "791--798",
    	keywords = "genetic algorithms, genetic programming",
    	month = {7-11 " # ju},
    	organisation = "SIGEVO",
    	address = "Philadelphia, Pennsylvania, USA",
    	doi = "doi:10.1145/2330163.2330273",
    	publisher = "ACM",
    	publisher_address = "New York, NY, USA",
    	abstract = "Genetic programming (GP) is not a field noted for the rigor of its benchmarking. Some of its benchmark problems are popular purely through historical contingency, and they can be criticized as too easy or as providing misleading information concerning real-world performance, but they persist largely because of inertia and the lack of good alternatives. Even where the problems themselves are impeccable, comparisons between studies are made more difficult by the lack of standardization. We argue that the definition of standard benchmarks is an essential step in the maturation of the field. We make several contributions towards this goal. We motivate the development of a benchmark suite and define its goals; we survey existing practice; we enumerate many candidate benchmarks; we report progress on reference implementations; and we set out a concrete plan for gathering feedback from the GP community that would, if adopted, lead to a standard set of benchmarks.",
    	notes = "GP Benchmarks Also known as \cite{2330273} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)"

Please, consult article for further details


For how to use it

help data_reg_bench

DATA_REG_BENCH Creates the training and testing data from symbolic
regression problems used in community benchmarks.

Input arguments:
problem - String corresponding to the wanted problem, match
names inside above article
trainfilename_x - Filename for input training data
trainfilename_y - Filename for output training data
testfilename_x - Filename for input testing data
testfilename_y - Filename for output testing data

Output arguments:
trainx - matrix or vector containing input training data
trainy - vector containing output training data
testx - matrix or vector containing input testing data
testy - vector containing output testing data
Copyright (C) 2013 Emigdio Z.Flores

This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License
as published by the Free Software Foundation; either version 2
of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
GNU General Public License for more details.

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