Analysing Symbolic Regression Benchmarks under a Meta-Learning Approach

Analysing Symbolic Regression Benchmarks under a Meta-Learning Approach

Luiz Otavio V. B. Oliveira, Joao Francisco B. S. Martins, Luis F. Miranda, Gisele L. Pappa Universidade Federal de Minas Gerais, Department of Computer ScienceBelo HorizonteBrazil [luizvbo, joaofbsm, luisfmiranda, glpappa]@dcc.ufmg.br
Abstract.

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, proposing a different approach to measure the quality of the symbolic regression benchmarks quantitatively. The proposed approach is based on meta-learning and uses a set of dataset meta-features—such as the number of examples or output skewness—to describe the datasets. Our idea is to correlate these meta-features with the errors obtained by a GP method. These meta-features define a space of benchmarks that should, ideally, have datasets (points) covering different regions of the space. An initial analysis of 63 datasets showed that current benchmarks are concentrated in a small region of this benchmark space. We also found out that number of instances and output skewness are the most relevant meta-features to GP output error. Both conclusions can help define which datasets should compose an effective testbed for symbolic regression methods.

copyright: acmlicenseddoi: isbn: 978-x-xxxx-xxxx-x/YY/MMconference: GECCO ’18; July 15-19, 2018; Kyoto, Japanjournalyear: 2018price: 15.00journalyear: 2018copyright: acmlicensedconference: Genetic and Evolutionary Computation Conference Companion; July 15–19, 2018; Kyoto, Japanprice: 15.00doi: 10.1145/3205651.3208293isbn: 978-1-4503-5764-7/18/07
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