- author = "Nuno Lourenco and Francisco B. Pereira and Ernesto Costa",
- title = "Learning Selection Strategies for Evolutionary Algorithms",
- booktitle = "Artificial Evolution, EA 2013",
- year = "2013",
- editor = "Pierrick Legrand and Marc-Michel Corsini and Jin-Kao Hao and Nicolas Monmarche and Evelyne Lutton and Marc Schoenauer",
- volume = "8752",
- series = "Lecture Notes in Computer Science",
- pre_print_pages = "226--237",
- pages = "197--208",
- address = "Bordeaux, France",
- month = "21-23 " # oct,
- organisation = "Association Evolution Artificielle",
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming, gramatical evolution, GE-HH, MKP",
- isbn13 = "9782953926736",
- isbn13 = "978-3-319-11682-2",
-
URL = "
http://ea2013.inria.fr/proceedings.pdf",
-
DOI = "
10.1007/978-3-319-11683-9_16",
- size = "12 pages",
- abstract = "Hyper-Heuristics is a recent area of research concerned with the automatic design of algorithms. In this paper we propose a grammar-based hyper-heuristic to automate the design of an Evolutionary Algorithm component, namely the parent selection mechanism. More precisely, we present a grammar that defines the number of individuals that should be selected, and how they should be chosen in order to adjust the selective pressure. Knapsack Problems are used to assess the capacity to evolve selection strategies. The results obtained show that the proposed approach is able to evolve general selection methods that are competitive with the ones usually described in the literature.",
-
notes = "Extended BNF grammar by adding ~ (repetition of
non-terminals) and & for numeric ranges. Multiple
Knapsck problem.
Old page numbers for paper 20 in pre-publication ISBN 9782953926736 http://ea2013.inria.fr/",
