Wong , Gary and Sharma, Anuraganand and Chandra, Rohitash (2018) Information Collection Strategies In Memetic Cooperative Neuroevolution For Time Series Prediction. [Conference Proceedings]
Full text not available from this repository. (Request a copy)Abstract
—Memetic algorithms have been a promising strategy
to enhance neuroevolution in the past. Cooperative coevolution
has been combined as memetic cooperative neuroevolution with
application to chaotic time series prediction. Although the method
has shown promising performance, there are limitations in the
balance between global and local search. The previous study used
a specific local search strategy for intensification that affected the
diversity of solutions. In this study, we address this limitation
by information (meme) collection strategies that maintains and
refines a pool of memes during global search. We present two
strategies where one is sequential and the other is concurrent
meme collection implemented at different stages of evolution.
In the majority of the given problems, the proposed strategies
showed improvement in prediction accuracy over the related
methods
| Item Type: | Conference Proceedings |
|---|---|
| Uncontrolled Keywords: | evolutionary computation;search problems;social sciences;time series;memetic algorithms;neuroevolution;coevolution;chaotic time series prediction;global search;specific local search strategy;information collection strategies;concurrent meme collection;prediction accuracy;Memetics;Time series analysis;Neurons;Prediction algorithms;Sociology;Cooperative Coevolution;Memetic Algorithms;Time Series Prediction Global Search;Local Search;Neuroevolution |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences |
| Depositing User: | Anuraganand Sharma |
| Date Deposited: | 17 Jul 2019 22:20 |
| Last Modified: | 17 Jul 2019 22:20 |
| URI: | https://repository.usp.ac.fj/id/eprint/11611 |
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