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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/222741
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- Title
- Adaptive range parameter control
- Author(s)
- Aleti, Aldeida; Moser, Irene; Mostaghim, Sanaz
- Abstract
- All existing stochastic optimisers such as Evolutionary Algorithms require parameterisation which has a significant influence on the algorithm's performance. In most cases, practitioners assign static values to variables after an initial tuning phase. This parameter tuning method requires experience the practitioner may not have and, when done conscientiously, is rather time-consuming. Also, the use of parameter values that remain constant over the optimisation process has been observed to achieve suboptimal results. This work presents a parameter control method which redefines variables repeatedly based on a separate optimisation process which receives its feedback from the primary optimisation algorithm. The feedback is used for a projection of the value performing well in the future. The parameter values are sampled from intervals which are adapted dynamically, a method which has proved particularly effective and outperforms all existing adaptive parameter controls significantly.
- Publication type
- Conference paper
- Research centre
- Swinburne University of Technology
- Source
- Proceedings of the 2012 IEEE Congress on Evolutionary Computation (CEC 2012), held in conjunction with the 2012 IEEE World Congress on Computational Intelligence (IEEE WCCI 2012), the 2012 International Joint Conference on Neural Networks (IJCNN 2012) and the 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2012), Brisbane, Queensland, Australia, 10-15 June 2012
- Publication year
- 2012
- Keyword(s)
- Evolutionary algorithms; Optimisation process; Parameter control methods; Parameter values; Stochastic optimisation methods
- Publisher
- IEEE
- ISBN
- 9781467315098, 1467315095
- Publisher URL
- http://dx.doi.org/10.1109/CEC.2012.6256567
- Copyright
- Copyright © 2012 IEEE. The accepted manuscript is reproduced in accordance with the copyright policy of the publisher. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
- Research Projects
-
Dependability optimization at an architectural level of system design, Cooperative Research Centre for Advanced Automotive Technology (AutoCRC) grant number C4-509
- Full text
- Peer reviewed
