The nature of statistical learning theory
The nature of statistical learning theory
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Methods and Applications of Interval Analysis (SIAM Studies in Applied and Numerical Mathematics) (Siam Studies in Applied Mathematics, 2.)
Accelerating evolutionary algorithms with Gaussian process fitness function models
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Interactive Evolutionary Computation-Based Hearing Aid Fitting
IEEE Transactions on Evolutionary Computation
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Hi-index | 0.00 |
In order to alleviate user fatigue of interactive genetic algorithms with an individual's fuzzy and stochastic fitness, we propose a surrogate model-assisted algorithm by using a directed fuzzy graph to extract user cognition. According to cut-set level and interval dominance probability, we present approaches to construct a directed fuzzy graph of an evolutionary population and calculate an individual's precise fitness based on it. By applying the fuzzy entropy, the chance of data sampling is achieved to obtain reliable samples for training the surrogate model. We adopt a support vector regression machine as the surrogate model, train it using the sampled individuals and their precise fitness, and apply a traditional genetic algorithm to optimize the surrogate model for some generations, providing guided individuals to the user to accelerate the evolution. We quantitatively analyze the performance of the presented algorithm in alleviating user fatigue and increasing more opportunities to look for the satisfactory individuals. Finally, we apply our algorithm to a fashion evolutionary design system to demonstrate its efficiency.