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Tuning of Multiple ...
Tuning of Multiple Parameter Sets in Evolutionary Algorithms
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- Andersson, Martin (författare)
- Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningscentrum för Virtuella system,Produktion och automatiseringsteknik, Production and Automation Engineering
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- Bandaru, Sunith (författare)
- Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningscentrum för Virtuella system,Produktion och automatiseringsteknik, Production and Automation Engineering
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- Ng, Amos H. C. (författare)
- Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningscentrum för Virtuella system,Produktion och automatiseringsteknik, Production and Automation Engineering
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(creator_code:org_t)
- 2016-07-20
- 2016
- Engelska.
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Ingår i: GECCO'16. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450342063 ; , s. 533-540
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Evolutionary optimization algorithms typically use one or more parameters that control their behavior. These parameters, which are often kept constant, can be tuned to improve the performance of the algorithm on specific problems. However, past studies have indicated that the performance can be further improved by adapting the parameters during runtime. A limitation of these studies is that they only control, at most, a few parameters, thereby missing potentially beneficial interactions between them. Instead of finding a direct control mechanism, the novel approach in this paper is to use different parameter sets in different stages of an optimization. These multiple parameter sets, which remain static within each stage, are tuned through extensive bi-level optimization experiments that approximate the optimal adaptation of the parameters. The algorithmic performance obtained with tuned multiple parameter sets is compared against that obtained with a single parameter set. For the experiments in this paper, the parameters of NSGA-II are tuned when applied to the ZDT, DTLZ and WFG test problems. The results show that using multiple parameter sets can significantly increase the performance over a single parameter set.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- evolutionary algorithms
- parameter tuning
- multiple parameters
- multi-objective optimization
- Production and Automation Engineering
- Produktion och automatiseringsteknik
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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GECCO'16
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