- title = "A Linear Estimation-of-Distribution {GP} System",
- author = "Riccardo Poli and Nicholas Freitag McPhee",
- bibdate = "2008-04-15",
- bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/eurogp/eurogp2008.html#PoliM08",
- booktitle = "Proceedings of the 11th European Conference on Genetic Programming, EuroGP 2008",
- address = "Naples",
- month = "26-28 " # mar,
- publisher = "Springer",
- year = "2008",
- volume = "4971",
- editor = "Michael O'Neill and Leonardo Vanneschi and Steven Gustafson and Anna Isabel {Esparcia Alcazar} and Ivanoe {De Falco} and Antonio {Della Cioppa} and Ernesto Tarantino",
- isbn13 = "978-3-540-78670-2",
- pages = "206--217",
- series = "Lecture Notes in Computer Science",
- keywords = "genetic algorithms, genetic programming",
-
DOI = "
doi:10.1007/978-3-540-78671-9_18",
- abstract = "We present N-gram GP, an estimation of distribution algorithm for the evolution of linear computer programs. The algorithm learns and samples a joint probability distribution of triplets of instructions (or 3-grams) at the same time as it is learning and sampling a program length distribution. We have tested N-gram GP on symbolic regressions problems where the target function is a polynomial of up to degree 12 and lawn-mower problems with lawn sizes of up to 12x12. Results show that the algorithm is effective and scales better on these problems than either linear GP or simple stochastic hill-climbing.",
-
notes = "Also known as \cite{conf/eurogp/PoliM08}
Part of \cite{conf/eurogp/2008} EuroGP'2008 held in conjunction with EvoCOP2008, EvoBIO2008 and EvoWorkshops2008",
