Conference paper

DRAHOŠOVÁ Michaela, HULVA Jiří and SEKANINA Lukáš. Indirectly Encoded Fitness Predictors Coevolved with Cartesian Programs. In: Genetic Programming. Berlin: Springer International Publishing, 2015, pp. 113-125. ISBN 978-3-319-16500-4. Available from:
Publication language:english
Original title:Indirectly Encoded Fitness Predictors Coevolved with Cartesian Programs
Title (cs):Koevoluce nepřímo kódovaných prediktorů fitness a kartézských programů
Proceedings:Genetic Programming
Conference:18th European Conference on Genetic Programming
Series:LNCS 9025
Place:Berlin, DE
Publisher:Springer International Publishing
coevolution, cartesian genetic programming, fitness prediction, symbolic regression
We investigate coevolutionary Cartesian genetic programming that coevolves fitness predictors in order to diminish the number of target objective vector (TOV) evaluations, needed to obtain a satisfactory solution, to reduce the computational cost of evolution. This paper introduces the use of coevolution of fitness predictors in CGP with a new type of indirectly encoded predictors. Indirectly encoded predictors are operated using the CGP and provide a variable number of TOVs used for solution evaluation during the coevolution. It is shown in 5 symbolic regression problems that the proposed predictors are able to adapt the size of TOVs array in response to a particular training data set.
   author = {Michaela Draho{\v{s}}ov{\'{a}} and Ji{\v{r}}{\'{i}} Hulva
	and Luk{\'{a}}{\v{s}} Sekanina},
   title = {Indirectly Encoded Fitness Predictors Coevolved with
	Cartesian Programs},
   pages = {113--125},
   booktitle = {Genetic Programming},
   series = {LNCS 9025},
   year = {2015},
   location = {Berlin, DE},
   publisher = {Springer International Publishing},
   ISBN = {978-3-319-16500-4},
   language = {english},
   url = {}

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