- author = "Michaela Sikulova and Lukas Sekanina",
- title = "Coevolution in Cartesian Genetic Programming",
- booktitle = "Proceedings of the 15th European Conference on Genetic Programming, EuroGP 2012",
- year = "2012",
- month = "11-13 " # apr,
- editor = "Alberto Moraglio and Sara Silva and Krzysztof Krawiec and Penousal Machado and Carlos Cotta",
- series = "LNCS",
- volume = "7244",
- publisher = "Springer Verlag",
- address = "Malaga, Spain",
- pages = "182--193",
- organisation = "EvoStar",
- isbn13 = "978-3-642-29138-8",
-
DOI = "
doi:10.1007/978-3-642-29139-5_16",
- size = "12 pages",
- keywords = "genetic algorithms, genetic programming, cartesian genetic programming, Coevolution, Symbolic regression",
- abstract = "Cartesian genetic programming (CGP) is a branch of genetic programming which has been used in various applications. This paper proposes to introduce coevolution to CGP in order to accelerate the task of symbolic regression. In particular, fitness predictors which are small subsets of the training set are coevolved with CGP programs. It is shown using five symbolic regression problems that the (median) execution time can be reduced 2--5 times in comparison with the standard CGP.",
-
notes = "1+lambda hill climber with point mutation on CGP
representation with restart. 1pt crossover?
Part of \cite{Moraglio:2012:GP} EuroGP'2012 held in conjunction with EvoCOP2012 EvoBIO2012, EvoMusArt2012 and EvoApplications2012",
