The nature of statistical learning theory
The nature of statistical learning theory
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Feature Selection for Support Vector Machines by Means of Genetic Algorithms
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
A decision support system based on support vector machines for diagnosis of the heart valve diseases
Computers in Biology and Medicine
Domain described support vector classifier for multi-classification problems
Pattern Recognition
Expert Systems with Applications: An International Journal
Interactive Evolutionary Computation-Based Hearing Aid Fitting
IEEE Transactions on Evolutionary Computation
ConBreO: a music performance rendering system using hybrid approach of IEC and automated evolution
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A genetic algorithm assisted by a locally weighted regression surrogate model
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part I
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Interactive genetic algorithms with individual's fuzzy fitness well portray the fuzzy uncertainties of a user's cognition. In this paper, we propose an efficient surrogate model-assisted one to alleviate user fatigue by building a classifier and a regressor to approximate the user's cognition. Two reliable training data sets are obtained based on the user's evaluation credibility. Then a support vector classification machine and a support vector regression machine are trained as the surrogate models with these samples. Specifically, the input trained samples are the individuals evaluated by the user, and the output training samples of the classifier and the regressor are widths and centers of these individuals' fuzzy fitness assigned by the user, respectively. These two surrogate models are simultaneously applied to the subsequent evolutions with enlarged population size so as to alleviate user fatigue and enhance the search ability of the algorithm. We constantly update the training data sets and the surrogate models in order to guarantee the approximation precision. Furthermore, we quantitatively analyze the algorithm's performance in alleviating user fatigue and increasing more opportunities to find the optimal solutions. We also apply it to a fashion evolutionary design system to show its efficiency.