his.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Parameter tuned CMA-ES on the CEC'15 expensive problems
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Produktion och automatiseringsteknik, Production and Automation Engineering)
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID-id: 0000-0001-5436-2128
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID-id: 0000-0003-0111-1776
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID-id: 0000-0003-3973-3394
2015 (Engelska)Ingår i: Evolutionary Computation, IEEE conference proceedings, 2015, s. 1950-1957Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Evolutionary optimization algorithms have parameters that are used to adapt the search strategy to suit different optimization problems. Selecting the optimal parameter values for a given problem is difficult without a-priori knowledge. Experimental studies can provide this knowledge by finding the best parameter values for a specific set of problems. This knowledge can also be constructed into heuristics (rule-of-thumbs) that can adapt the parameters for the problem. The aim of this paper is to assess the heuristics of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimization algorithm. This is accomplished by tuning CMA-ES parameters so as to maximize its performance on the CEC'15 problems, using a bilevel optimization approach that searches for the optimal parameter values. The optimized parameter values are compared against the parameter values suggested by the heuristics. The difference between specialized and generalized parameter values are also investigated.

Ort, förlag, år, upplaga, sidor
IEEE conference proceedings, 2015. s. 1950-1957
Nyckelord [en]
Parameter tuning, CMA-ES
Nationell ämneskategori
Data- och informationsvetenskap
Forskningsämne
Teknik; Produktion och automatiseringsteknik
Identifikatorer
URN: urn:nbn:se:his:diva-11599DOI: 10.1109/CEC.2015.7257124ISI: 000380444801129Scopus ID: 2-s2.0-84963626635ISBN: 978-1-4799-7492-4 (tryckt)OAI: oai:DiVA.org:his-11599DiVA, id: diva2:860215
Konferens
2015 IEEE Congress on Evolutionary Computation (CEC)
Tillgänglig från: 2015-10-12 Skapad: 2015-10-12 Senast uppdaterad: 2018-11-07Bibliografiskt granskad
Ingår i avhandling
1. A bilevel approach to parameter tuning of optimization algorithms using evolutionary computing: Understanding optimization algorithms through optimization
Öppna denna publikation i ny flik eller fönster >>A bilevel approach to parameter tuning of optimization algorithms using evolutionary computing: Understanding optimization algorithms through optimization
2018 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

Most optimization problems found in the real world cannot be solved using analytical methods. For these types of difficult optimization problems, an alternative approach is needed. Metaheuristics are a category of optimization algorithms that do not guarantee that an optimal solution will be found, but instead search for the best solutions using some general heuristics. Metaheuristics have been shown to be effective at finding “good-enough” solutions to a wide variety of difficult problems. Most metaheuristics involve control parameters that can be used to modify how the heuristics perform its search. This is necessary because different problems may require different search strategies to be solved effectively. The control parameters allow for the optimization algorithm to be adapted to the problem at hand. It is, however, difficult to predict what the optimal control parameters are for any given problem. The problem of finding these optimal control parameter values is known as parameter tuning and is the main topic of this thesis. This thesis uses a bilevel optimization approach to solve parameter tuning problems. In this approach, the parameter tuning problem itself is formulated as an optimization problem and solved with an optimization algorithm. The parameter tuning problem formulated as a bilevel optimization problem is challenging because of nonlinear objective functions, interacting variables, multiple local optima, and noise. However, it is in precisely this kind of difficult optimization problem that evolutionary algorithms, which are a subclass of metaheuristics, have been shown to be effective. That is the motivation for using evolutionary algorithms for the upper-level optimization (i.e. tuning algorithm) of the bilevel optimization approach. Solving the parameter tuning problem using a bilevel optimization approach is also computationally expensive, since a complete optimization run has to be completed for every evaluation of a set of control parameter values. It is therefore important that the tuning algorithm be as efficient as possible, so that the parameter tuning problem can be solved to a satisfactory level with relatively few evaluations. Even so, bilevel optimization experiments can take a long time to run on a single computer. There is, however, considerable parallelization potential in the bilevel optimization approach, since many of the optimizations are independent of one another. This thesis has three primary aims: first, to present a bilevel optimization framework and software architecture for parallel parameter tuning; second, to use this framework and software architecture to evaluate and configure evolutionary algorithms as tuners and compare them with other parameter tuning methods; and, finally, to use parameter tuning experiments to gain new insights into and understanding of how optimization algorithms work and how they can used be to their maximum potential. The proposed framework and software architecture have been implemented and deployed in more than one hundred computers running many thousands of parameter tuning experiments for many millions of optimizations. This illustrates that this design and implementation approach can handle large parameter tuning experiments. Two types of evolutionary algorithms, i.e. differential evolution (DE) and a genetic algorithm (GA), have been evaluated as tuners against the parameter tuning algorithm irace. The as pects of algorithm configuration and noise handling for DE and the GA as related to the parameter tuning problem were also investigated. The results indicate that dynamic resampling strategies outperform static resampling strategies. It was also shown that the GA needs an explicit exploration and exploitation strategy in order not become stuck in local optima. The comparison with irace shows that both DE and the GA can significantly outperform it in a variety of different tuning problems.

Ort, förlag, år, upplaga, sidor
Skövde: University of Skövde, 2018. s. 210
Serie
Dissertation Series ; 25
Nationell ämneskategori
Systemvetenskap, informationssystem och informatik med samhällsvetenskaplig inriktning
Forskningsämne
Produktion och automatiseringsteknik
Identifikatorer
urn:nbn:se:his:diva-16368 (URN)978-91-984187-7-4 (ISBN)
Disputation
2018-09-24, ASSAR Industrial Innovation Arena, Skövde, 10:00
Opponent
Handledare
Tillgänglig från: 2018-11-15 Skapad: 2018-11-07 Senast uppdaterad: 2018-11-15Bibliografiskt granskad

Open Access i DiVA

fulltext(540 kB)481 nedladdningar
Filinformation
Filnamn FULLTEXT01.pdfFilstorlek 540 kBChecksumma SHA-512
04faefaa337e0190ecab89df723b961456d3b0a7e5424a20fd82fb86e906522a7e9084ffe107d5375566b6bb6dea88996529a6812fcd1fec160b35a8ce49cda0
Typ fulltextMimetyp application/pdf

Övriga länkar

Förlagets fulltextScopus

Personposter BETA

Andersson, MartinBandaru, SunithNg, Amos H. C.Syberfeldt, Anna

Sök vidare i DiVA

Av författaren/redaktören
Andersson, MartinBandaru, SunithNg, Amos H. C.Syberfeldt, Anna
Av organisationen
Institutionen för ingenjörsvetenskapForskningscentrum för Virtuella system
Data- och informationsvetenskap

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 481 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

doi
isbn
urn-nbn

Altmetricpoäng

doi
isbn
urn-nbn
Totalt: 1668 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf