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20162020
most citedHow Noisy Data Affects Geometric Semantic Genetic Programming

7 citations · 14 across the 5 of their papers we have counts for

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cs.NE2020

Metaheuristics "In the Large"

Jerry Swan, Steven Adriaensen, Alexander E. I. Brownlee +13

Following decades of sustained improvement, metaheuristics are one of the great success stories of optimization research. However, in order for research in metaheuristics to avoid…

cs.NE2020

Neural Architecture Search in Graph Neural Networks

Matheus Nunes, Gisele L. Pappa

Performing analytical tasks over graph data has become increasingly interesting due to the ubiquity and large availability of relational information. However, unlike images or sent…

cs.NE2018

Analysing Symbolic Regression Benchmarks under a Meta-Learning Approach

Luiz Otavio Vilas Boas Oliveira, Joao Francisco Barreto da Silva Martins, Luis Fernando Miranda +1

The definition of a concise and effective testbed for Genetic Programming (GP) is a recurrent matter in the research community. This paper takes a new step in this direction, propo…

cs.NE2018

Solving the Exponential Growth of Symbolic Regression Trees in Geometric Semantic Genetic Programming

Joao Francisco B. S. Martins, Luiz Otavio V. B. Oliveira, Luis F. Miranda +2

Advances in Geometric Semantic Genetic Programming (GSGP) have shown that this variant of Genetic Programming (GP) reaches better results than its predecessor for supervised machin…

cs.NE2017★ 7 cited

How Noisy Data Affects Geometric Semantic Genetic Programming

Luis F. Miranda, Luiz Otavio V. B. Oliveira, Joao Francisco B. S. Martins +1

Noise is a consequence of acquiring and pre-processing data from the environment, and shows fluctuations from different sources---e.g., from sensors, signal processing technology o…