Analyzing the effectiveness and applicability of co-training
Proceedings of the ninth international conference on Information and knowledge management
Radial Basis Functions
Beyond the point cloud: from transductive to semi-supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Semisupervised Regression with Cotraining-Style Algorithms
IEEE Transactions on Knowledge and Data Engineering
Graph-theoretic measure for active iGAs: interaction sizing and parallel evaluation ensemble
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Analyzing Co-training Style Algorithms
ECML '07 Proceedings of the 18th European conference on Machine Learning
A Survey of Semi-Supervised Learning Methods
CIS '08 Proceedings of the 2008 International Conference on Computational Intelligence and Security - Volume 02
Interactive genetic algorithms with variational population size
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Local function approximation in evolutionary algorithms for the optimization of costly functions
IEEE Transactions on Evolutionary Computation
Interactive Evolutionary Computation-Based Hearing Aid Fitting
IEEE Transactions on Evolutionary Computation
A hybrid OC-GA approach for fast and global truss optimization with frequency constraints
Applied Soft Computing
Crossover method for interactive genetic algorithms to estimate multimodal preferences
Applied Computational Intelligence and Soft Computing
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Interactive genetic algorithms are effective methods of solving optimization problems with implicit (qualitative) criteria by incorporating a user's intelligent evaluation into traditional evolution mechanisms. The heavy evaluation burden of the user, however, is crucial and limits their applications in complex optimization problems. We focus on reducing the evaluation burden by presenting a semi-supervised learning assisted interactive genetic algorithm with large population. In this algorithm, a population with many individuals is adopted to efficiently explore the search space. A surrogate model built with an improved semi-supervised learning method is employed to evaluate a part of individuals instead of the user to alleviate his/her burden in evaluation. Incorporated with the principles of the improved semi-supervised learning, the opportunities of applying and updating the surrogate model are determined by its confidence degree in estimation, and the informative individuals reevaluated by the user are selected according to the concept of learning from mistakes. We quantitatively analyze the performance of the proposed algorithm and apply it to the design of sunglasses lenses, a representative optimization problem with one qualitative criterion. The empirical results demonstrate the strength of our algorithm in searching for satisfactory solutions and easing the evaluation burden of the user.