- author = "Martin Zaefferer and Joerg Stork and Oliver Flasch and Thomas Bartz-Beielstein",
- title = "Linear Combination of Distance Measures for Surrogate Models in Genetic Programming",
- booktitle = "15th International Conference on Parallel Problem Solving from Nature",
- year = "2018",
- editor = "Anne Auger and Carlos M. Fonseca and Nuno Lourenco and Penousal Machado and Luis Paquete and Darrell Whitley",
- volume = "11102",
- series = "LNCS",
- pages = "220--231",
- address = "Coimbra, Portugal",
- month = "8-12 " # sep,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, Surrogate models, Distance measures",
- isbn13 = "978-3-319-99258-7",
-
URL = "
https://www.springer.com/gp/book/9783319992587",
-
DOI = "
doi:10.1007/978-3-319-99259-4_18",
-
abstract = "Surrogate models are a well established approach to
reduce the number of expensive function evaluations in
continuous optimization. In the context of genetic
programming, surrogate modelling still poses a
challenge, due to the complex genotype-phenotype
relationships. We investigate how different genotypic
and phenotypic distance measures can be used to learn
Kriging models as surrogates. We compare the measures
and suggest to use their linear combination in a
kernel.
We test the resulting model in an optimization framework, using symbolic regression problem instances as a benchmark. Our experiments show that the model provides valuable information. Firstly, the model enables an improved optimization performance compared to a model-free algorithm. Furthermore, the model provides information on the contribution of different distance measures. The data indicates that a phenotypic distance measure is important during the early stages of an optimization run when less data is available. In contrast, genotypic measures, such as the tree edit distance, contribute more during the later stages.",
-
notes = "See also arXiv:1807.01019
PPSN2018 http://ppsn2018.dei.uc.pt
This two-volume set LNCS 11101 and 11102 constitutes the refereed proceedings of the 15th International Conference on Parallel Problem Solving from Nature, PPSN 2018",
