NDLI: Semi-supervised ranking SVM-assisted IGA with hierarchical evaluations

Content Provider	IEEE Xplore Digital Library
Author	Xiao-yan Sun Shan-shan Chen Xiao-ping Ma
Copyright Year	2012
Description	Author affiliation: School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China 221116 (Xiao-yan Sun; Shan-shan Chen; Xiao-ping Ma)
Abstract	Interactive genetic algorithms (IGAs), combining the human's intelligent evaluations with the evolutionary optimization mechanisms, have been sufficiently investigated due to their powerful performance in solving problems with aesthetic criteria. User fatigue arising from intelligent evaluations, however, greatly restricts IGAs to successfully optimize complicated problems. Lot of efforts have been devoted to effectively alleviating user fatigue and improving the exploration performance by designing friendly evaluation modes or surrogate models. A large population IGA with a ranking learning based surrogate model is presented here. A hierarchical evaluation mode reflecting the preference orderings of the user is first presented. From the perspective of directly maintaining the correct preference ordering, a ranking function is constructed to estimate the fitness of all individuals. An enhanced semi-supervised ranking SVM is developed to obtain the ranking function with high prediction accuracy, in which the method of selecting most informative unlabeled samples is highlighted by naturally combining the merits of IGAs. The ranking function is managed based on a defined metric of prediction accuracy. The proposed algorithm is applied to a fashion design system to empirically demonstrate its strength in alleviating user fatigue and improving exploration.
Starting Page	1
Ending Page	8
File Size	977567
Page Count	8
File Format	PDF
ISBN	9781467315104
e-ISBN	9781467315098
e-ISBN	9781467315081
DOI	10.1109/CEC.2012.6256106
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-10
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Optimization Fatigue Algorithm design and analysis Approximation methods Educational institutions
Content Type	Text
Resource Type	Article

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