A Benchmark with Facile Adjustment of Difficulty for Many-Objective Genetic Programming and Its Reference Set
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @InProceedings{Ohki:2020:GECCOcomp,
-
author = "Makoto Ohki",
-
title = "A Benchmark with Facile Adjustment of Difficulty for
Many-Objective Genetic Programming and Its Reference
Set",
-
year = "2020",
-
editor = "Richard Allmendinger and Hugo Terashima Marin and
Efren Mezura Montes and Thomas Bartz-Beielstein and
Bogdan Filipic and Ke Tang and David Howard and
Emma Hart and Gusz Eiben and Tome Eftimov and
William {La Cava} and Boris Naujoks and Pietro Oliveto and
Vanessa Volz and Thomas Weise and Bilel Derbel and Ke Li and
Xiaodong Li and Saul Zapotecas and Qingfu Zhang and
Rui Wang and Ran Cheng and Guohua Wu and Miqing Li and
Hisao Ishibuchi and Jonathan Fieldsend and
Ozgur Akman and Khulood Alyahya and Juergen Branke and
John R. Woodward and Daniel R. Tauritz and Marco Baioletti and
Josu Ceberio Uribe and John McCall and
Alfredo Milani and Stefan Wagner and Michael Affenzeller and
Bradley Alexander and Alexander (Sandy) Brownlee and
Saemundur O. Haraldsson and Markus Wagner and
Nayat Sanchez-Pi and Luis Marti and Silvino {Fernandez Alzueta} and
Pablo {Valledor Pellicer} and Thomas Stuetzle and
Matthew Johns and Nick Ross and Ed Keedwell and
Herman Mahmoud and David Walker and Anthony Stein and
Masaya Nakata and David Paetzel and Neil Vaughan and
Stephen Smith and Stefano Cagnoni and Robert M. Patton and
Ivanoe {De Falco} and Antonio {Della Cioppa} and
Umberto Scafuri and Ernesto Tarantino and
Akira Oyama and Koji Shimoyama and Hemant Kumar Singh and
Kazuhisa Chiba and Pramudita Satria Palar and Alma Rahat and
Richard Everson and Handing Wang and Yaochu Jin and
Erik Hemberg and Riyad Alshammari and
Tokunbo Makanju and Fuijimino-shi and Ivan Zelinka and Swagatam Das and
Ponnuthurai Nagaratnam and Roman Senkerik",
-
isbn13 = "9781450371278",
-
publisher = "Association for Computing Machinery",
-
publisher_address = "New York, NY, USA",
-
URL = "
https://doi.org/10.1145/3377929.3398080",
-
DOI = "
doi:10.1145/3377929.3398080",
-
booktitle = "Proceedings of the 2020 Genetic and Evolutionary
Computation Conference Companion",
-
pages = "1915--1922",
-
size = "8 pages",
-
keywords = "genetic algorithms, genetic programming,
many-objective knapsack problem, many-objective genetic
programming, analytic function",
-
address = "internet",
-
series = "GECCO '20",
-
month = jul # " 8-12",
-
organisation = "SIGEVO",
-
abstract = "In several works, Multi-Objective GP (MOGP) using
Multi-Objective Evolutionary Algorithms (MOEAs) is
effective on function estimation problems for the
cutting process of steel, modeling of non-linear
systems, and truss optimization. However, their targets
are two or three objective GP problems. Only little
research on GP problems with more than four objectives,
or Many-Objective Genetic Programming (MaOGP), exists.
This is not because real MaOGP problems are ere are
rare, but probably because there are few MaOGP
benchmarks. Therefore, this paper proposes a benchmark
for MaOGP. The problem consists of an analytic function
generated by GP and the well-known Many-Objective
KnapSack Problem (MaOKSP). In this problem, the
difficulty of the problem can be easily adjusted by
changing the non-terminal node set, the number of
knapsacks or the number of objectives, the number of
items, and so on. Moreover, the IGD# indicator is
proposed, in which this is a slightly improved version
of the IGD+.",
-
notes = "Also known as \cite{10.1145/3377929.3398080}
GECCO-2020 A Recombination of the 29th International
Conference on Genetic Algorithms (ICGA) and the 25th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Makoto Ohki
Citations
