Runtime analysis of mutation-based geometric semantic genetic programming for basis functions regression
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{Moraglio:2013:GECCO,
-
author = "Alberto Moraglio and Andrea Mambrini",
-
title = "Runtime analysis of mutation-based geometric semantic
genetic programming for basis functions regression",
-
booktitle = "GECCO '13: Proceeding of the fifteenth annual
conference on Genetic and evolutionary computation
conference",
-
year = "2013",
-
editor = "Christian Blum and Enrique Alba and Anne Auger and
Jaume Bacardit and Josh Bongard and Juergen Branke and
Nicolas Bredeche and Dimo Brockhoff and
Francisco Chicano and Alan Dorin and Rene Doursat and
Aniko Ekart and Tobias Friedrich and Mario Giacobini and
Mark Harman and Hitoshi Iba and Christian Igel and
Thomas Jansen and Tim Kovacs and Taras Kowaliw and
Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and
John McCall and Alberto Moraglio and
Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and
Gustavo Olague and Yew-Soon Ong and
Michael E. Palmer and Gisele Lobo Pappa and
Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and
Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and
Daniel Tauritz and Leonardo Vanneschi",
-
isbn13 = "978-1-4503-1963-8",
-
pages = "989--996",
-
keywords = "genetic algorithms, genetic programming",
-
month = "6-10 " # jul,
-
organisation = "SIGEVO",
-
address = "Amsterdam, The Netherlands",
-
DOI = "doi:10.1145/2463372.2463492",
-
publisher = "ACM",
-
publisher_address = "New York, NY, USA",
-
abstract = "Geometric Semantic Genetic Programming (GSGP) is a
recently introduced form of Genetic Programming (GP)
that searches the semantic space of functions/programs.
The fitness landscape seen by GSGP is always, for any
domain and for any problem, unimodal with a linear
slope by construction. This makes the search for the
optimum much easier than for traditional GP, and it
opens the way to analyse theoretically in a easy manner
the optimisation time of GSGP in a general setting.
Very recent work proposed a runtime analysis of
mutation-based GSGP on the class of all Boolean
functions. We present a runtime analysis of
mutation-based GSGP on the class of all regression
problems with generic basis functions (encompassing
e.g., polynomial regression and trigonometric
regression).",
-
notes = "Also known as \cite{2463492} GECCO-2013 A joint
meeting of the twenty second international conference
on genetic algorithms (ICGA-2013) and the eighteenth
annual genetic programming conference (GP-2013)",
- }
Genetic Programming entries for
Alberto Moraglio
Andrea Mambrini
Citations