- author = "Luiz Otavio V. B. Oliveira and Fernando E. B. Otero and Gisele L. Pappa and Julio Albinati",
- title = "Sequential Symbolic Regression with Genetic Programming",
- booktitle = "Genetic Programming Theory and Practice XII",
- year = "2014",
- editor = "Rick Riolo and William P. Worzel and Mark Kotanchek",
- series = "Genetic and Evolutionary Computation",
- pages = "73--90",
- address = "Ann Arbor, USA",
- month = "8-10 " # may,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, Symbolic Regression, Semantic Genetic Programming, Geometric Semantic Crossover, Problem Transformation",
- isbn13 = "978-3-319-16029-0",
-
DOI = "
10.1007/978-3-319-16030-6_5",
- abstract = "This chapter describes the Sequential Symbolic Regression (SSR) method, a new strategy for function approximation in symbolic regression. The SSR method is inspired by the sequential covering strategy from machine learning, but instead of sequentially reducing the size of the problem being solved, it sequentially transforms the original problem into potentially simpler problems. This transformation is performed according to the semantic distances between the desired and obtained outputs and a geometric semantic operator. The rationale behind SSR is that, after generating a suboptimal function f via symbolic regression, the output errors can be approximated by another function, in a subsequent iteration. The method was tested in eight polynomial functions, and compared with canonical genetic programming (GP) and geometric semantic genetic programming (SGP). Results showed that SSR significantly outperforms SGP and presents no statistical difference from GP. More importantly, they show the potential of the proposed approach: an effective way of applying geometric semantic operators to combine different (partial) solutions, and at the same time, avoiding the exponential growth problem arising from the use of semantic operators.",
-
notes = "
Part of \cite{Riolo:2014:GPTP} published after the workshop in 2015",
