- author = "Thomas D. Griffiths and Aniko Ekart",
- title = "Self-Adaptive Crossover in Genetic Programming: The Case of the {Tartarus} Problem",
- 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 = "11101",
- series = "LNCS",
- pages = "236--246",
- address = "Coimbra, Portugal",
- month = "8-12 " # sep,
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, Self-adaptation, Crossover, Tartarus problem",
- isbn13 = "978-3-319-99252-5",
-
URL = "
https://www.springer.com/gp/book/9783319992587",
-
DOI = "
doi:10.1007/978-3-319-99253-2_19",
- abstract = "The runtime performance of many evolutionary algorithms depends heavily on their parameter values, many of which are problem specific. Previous work has shown that the modification of parameter values at runtime can lead to significant improvements in performance. In this paper we discuss both the when and how aspects of implementing self-adaptation in a Genetic Programming system, focusing on the crossover operator. We perform experiments on Tartarus Problem instances and find that the runtime modification of crossover parameters at the individual level, rather than population level, generate solutions with superior performance, compared to traditional crossover methods.",
-
notes = "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",
