- author = "Julio Albinati and Gisele L. Pappa and Fernando E. B. Otero and Luiz Otavio V. B. Oliveira",
- title = "The Effect of Distinct Geometric Semantic Crossover Operators in Regression Problems",
- booktitle = "18th European Conference on Genetic Programming",
- year = "2015",
- editor = "Penousal Machado and Malcolm I. Heywood and James McDermott and Mauro Castelli and Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim",
- series = "LNCS",
- volume = "9025",
- publisher = "Springer",
- pages = "3--15",
- address = "Copenhagen",
- month = "8-10 " # apr,
- organisation = "EvoStar",
- keywords = "genetic algorithms, genetic programming, Crossover, Crossover mask optimisation",
- isbn13 = "978-3-319-16500-4",
-
DOI = "
doi:10.1007/978-3-319-16501-1",
- abstract = "This paper investigates the impact of geometric semantic crossover operators in a wide range of symbolic regression problems. First, it analyses the impact of using Manhattan and Euclidean distance geometric semantic crossovers in the learning process. Then, it proposes two strategies to numerically optimise the crossover mask based on mathematical properties of these operators, instead of simply generating them randomly. An experimental analysis comparing geometric semantic crossovers using Euclidean and Manhattan distances and the proposed strategies is performed in a test bed of twenty datasets. The results show that the use of different distance functions in the semantic geometric crossover has little impact on the test error, and that our optimized crossover masks yield slightly better results. For SGP practitioners, we suggest the use of the semantic crossover based on the Euclidean distance, as it achieved similar results to those obtained by more complex operators.",
-
notes = "Nominated for EuroGP 2015 Best Paper.
Part of \cite{Machado:2015:GP} EuroGP'2015 held in conjunction with EvoCOP2015, EvoMusArt2015 and EvoApplications2015",
