Greedy Semantic Local Search for Small Solutions

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{Ffrancon:2015:GECCOcomp,

• author = "Robyn Ffrancon and Marc Schoenauer",
• title = "Greedy Semantic Local Search for Small Solutions",
• booktitle = "GECCO 2015 Semantic Methods in Genetic Programming (SMGP'15) Workshop",
• year = "2015",
• editor = "Colin Johnson and Krzysztof Krawiec and Alberto Moraglio and Michael O'Neill",
• isbn13 = "978-1-4503-3488-4",
• keywords = "genetic algorithms, genetic programming, Semantic Methods",
• pages = "1293--1300",
• month = "11-15 " # jul,
• organisation = "SIGEVO",
• address = "Madrid, Spain",
• URL = "https://hal.inria.fr/UMR8623/hal-01169074v1",
• URL = "http://doi.acm.org/10.1145/2739482.2768504",
• DOI = "doi:10.1145/2739482.2768504",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "Semantic Backpropagation (SB) was introduced in GP so as to take into account the semantics of a GP tree at all intermediate states of the program execution, i.e., at each node of the tree. The idea is to compute the optimal should-be values each subtree should return, whilst assuming that the rest of the tree is unchanged, and to choose a subtree that matches as well as possible these target values. A single tree is evolved by iteratively replacing one of its nodes with the best subtree from a static library according to this local fitness, with tree size as a secondary criterion. Previous results for standard Boolean GP benchmarks that have been obtained by the authors with another variant of SB are improved in term of tree size. SB is then applied for the first time to categorical GP benchmarks, and outperforms all known results to date for three variable finite algebras.",
• notes = "Also known as \cite{2768504} Distributed at GECCO-2015.",

}

Genetic Programming entries for Robyn Ffrancon Marc Schoenauer

Citations