Large Scale Parameter Meta-Optimization of Metaheuristic Optimization Algorithms with HeuristicLab Hive
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{2743,
-
author = "Christoph Neumueller and Andreas Scheibenpflug and
Stefan Wagner and Andreas Beham and
Michael Affenzeller",
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title = "Large Scale Parameter Meta-Optimization of
Metaheuristic Optimization Algorithms with HeuristicLab
Hive",
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booktitle = "Proceedings of the 8th spanish congress on
Metaheuristics, Evolutionary and Bioinspired Algorithms
(MAEB'2012)",
-
year = "2012",
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editor = "Jose Antonio Gamez Martin and
Jose Miguel Puerta Callejon",
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address = "Albacete, Spain",
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month = "8-10 " # feb,
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organisation = "Grupo de Sistemas Inteligentes y Mineria de Datos,
perteneciente a la Universidad de Castilla-La Mancha
(UCLM), Spain",
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keywords = "genetic algorithms, genetic programming, PMO",
-
URL = "http://research.fh-ooe.at/files/publications/2743_MAEB2012.pdf",
-
URL = "https://www.researchgate.net/profile/Andreas_Beham/publication/230701425_Large_Scale_Parameter_Meta-Optimization_of_Metaheuristic_Optimization_Algorithms_with_HeuristicLab_Hive/links/00b7d5332f7035c977000000/Large-Scale-Parameter-Meta-Optimization-of-Metaheuristic-Optimization-Algorithms-with-HeuristicLab-Hive.pdf",
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code_url = "http://dev.heuristiclab.com",
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size = "8 pages",
-
abstract = "many different metaheuristic algorithms have been
developed and applied to various problems. According to
the no free lunch theorem no single algorithm exists
that can solve all problems better than all other
algorithms. This is one of the reasons why
metaheuristic algorithms often have parameters which
allow them to change their behaviour in a certain
range. However, finding good parameter values is not
trivial and requires human expertise as well as time.
The search for optimal parameter values can be seen as
an optimisation problem itself which can be solved by a
metaheuristic optimization algorithm
(meta-optimisation). metaoptimization implementation
for the heuristic optimisation environment
HeuristicLab. Because meta-optimization is extremely
run time intensive, a distributed computation
infrastructure, HeuristicLabHive, is used and will be
described. To demonstrate the effectiveness of the
implementation, a number of parameter optimisation
experiments are performed and analysed.",
-
notes = "MAEB-2012 broken 2021
http://simd.albacete.org/maeb2012/index.php/sepconferencia",
- }
Genetic Programming entries for
Christoph Neumueller
Andreas Scheibenpflug
Stefan Wagner
Andreas Beham
Michael Affenzeller
Citations