- author = "Leonardo Vanneschi and Mauro Castelli and Luca Manzoni and Sara Silva and Leonardo Trujillo",
- title = "Is k Nearest Neighbours Regression Better than {GP}?",
- booktitle = "EuroGP 2020: Proceedings of the 23rd European Conference on Genetic Programming",
- year = "2020",
- month = "15-17 " # apr,
- editor = "Ting Hu and Nuno Lourenco and Eric Medvet",
- series = "LNCS",
- volume = "12101",
- publisher = "Springer Verlag",
- address = "Seville, Spain",
- pages = "244--261",
- organisation = "EvoStar, Species",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-3-030-44093-0",
-
video_url = "
https://youtu.be/gavQB2XAoac",
-
video_url = "https://youtu.be/rbUN4JRv02M",
-
DOI = "
doi:10.1007/978-3-030-44094-7_16",
- abstract = "This work starts from the empirical observation that k nearest neighbours (KNN) consistently outperforms state-of-the-art techniques for regression, including geometric semantic genetic programming (GSGP). However, KNN is a memorization, and not a learning, method, i.e. it evaluates unseen data on the basis of training observations, and not by running a learned model. This paper takes a first step towards the objective of defining a learning method able to equal KNN, by defining a new semantic mutation, called random vectors-based mutation (RVM). GP using RVM, called RVMGP, obtains results that are comparable to KNN, but still needs training data to evaluate unseen instances. A comparative analysis sheds some light on the reason why RVMGP outperforms GSGP, revealing that RVMGP is able to explore the semantic space more uniformly. This finding opens a question for the future: is it possible to define a new genetic operator, that explores the semantic space as uniformly as RVM does, but that still allows us to evaluate unseen instances without using training data?",
-
notes = "https://youtu.be/gavQB2XAoac
http://www.evostar.org/2020/cfp_eurogp.php Part of \cite{Hu:2020:GP} EuroGP'2020 held in conjunction with EvoCOP2020, EvoMusArt2020 and EvoApplications2020",
