A New Surrogate-Assisted Interactive Genetic Algorithm With Weighted Semisupervised Learning

Surrogate-assisted interactive genetic algorithms (IGAs) are found to be very effective in reducing human fatigue. Different from models used in most surrogate-assisted evolutionary algorithms, surrogates in IGA must be able to handle the inherent uncertainties in fitness assignment by human users,...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2013-04, Vol.43 (2), p.685-698
Main Authors: Sun, Xiaoyan, Gong, Dunwei, Jin, Yaochu, Chen, Shanshan
Format: Article
Language:English
Subjects:
Algorithms
Computer-Aided Design
Cybernetics
Fatigue
Fatigue (materials)
Fitness
Genetic algorithms
Human
Humans
Interactive genetic algorithms (GAs) (IGAs)
interval-based fitness
Machine Learning
Mathematical analysis
Models, Theoretical
Reliability
semisupervised learning (SSL)
Sociology
Statistics
Studies
surrogate-assisted evolutionary optimization
Training
Uncertainty
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Summary:Surrogate-assisted interactive genetic algorithms (IGAs) are found to be very effective in reducing human fatigue. Different from models used in most surrogate-assisted evolutionary algorithms, surrogates in IGA must be able to handle the inherent uncertainties in fitness assignment by human users, where, e.g., interval-based fitness values are assigned to individuals. This poses another challenge to using surrogates for fitness approximation in evolutionary optimization, in addition to the lack of training data. In this paper, a new surrogate-assisted IGA has been proposed, where the uncertainty in subjective fitness evaluations is exploited both in training the surrogates and in managing surrogates. To enhance the approximation accuracy of the surrogates, an improved cotraining algorithm for semisupervised learning has been suggested, where the uncertainty in interval-based fitness values is taken into account in training and weighting the two cotrained models. Moreover, uncertainty in the interval-based fitness values is also considered in model management so that not only the best individuals but also the most uncertain individuals will be chosen to be re-evaluated by the human user. The effectiveness of the proposed algorithm is verified on two test problems as well as in fashion design, a typical application of IGA. Our results indicate that the new surrogate-assisted IGA can effectively alleviate user fatigue and is more likely to find acceptable solutions in solving complex design problems.
ISSN:2168-2267
2168-2275
DOI:10.1109/TSMCB.2012.2214382