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Modelling fish habitat preference with a genetic algorithm-optimized Takagi-Sugeno model based on pairwise comparisons
Fukuda, S.; Waegeman, W.; Mouton, A.; De Baets, B. (2011). Modelling fish habitat preference with a genetic algorithm-optimized Takagi-Sugeno model based on pairwise comparisons, in: Melo-Pinto, P. et al. Eurofuse 2011: Workshop on Fuzzy Methods for Knowledge-Based Systems. Advances in Intelligent and Soft Computing, 107: pp. 375-387. http://dx.doi.org/10.1007/978-3-642-24001-0_34
In: Melo-Pinto, P. et al. (Ed.) (2011). Eurofuse 2011: Workshop on Fuzzy Methods for Knowledge-Based Systems. Advances in Intelligent and Soft Computing, 107. Springer: Berlin. ISBN 978-3-642-24001-0. XII, 426 pp. http://dx.doi.org/10.1007/978-3-642-24001-0, more
In: Advances in Intelligent and Soft Computing. Springer: Berlin. ISSN 1867-5662, more
Peer reviewed article  
Available in  Authors 
Keyword
    Marine/Coastal
Authors  Top 
  • Fukuda, S.
  • Waegeman, W.
  • Mouton, A., more
  • De Baets, B., more
Abstract
    Species-environment relationships are used for evaluating the current status of target species and the potential impact of natural or anthropogenic changes of their habitat. Recent researches reported that the results are strongly affected by the quality of a data set used. The present study attempted to apply pairwise comparisons to modelling fish habitat preference with Takagi-Sugeno-type fuzzy habitat preference models (FHPMs) optimized by a genetic algorithm (GA). The model was compared with the result obtained from the FHPM optimized based on mean squared error (MSE). Three independent data sets were used for training and testing of these models. The FHPMs based on pairwise comparison produced variable habitat preference curves from 20 different initial conditions in the GA. This could be partially ascribed to the optimization process and the regulations assigned. This case study demonstrates applicability and limitations of pairwise comparison-based optimization in an FHPM. Future research should focus on a more flexible learning process to make a good use of the advantages of pairwise comparisons.
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