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Shangfei Wang

Publications and source records attributed to Shangfei Wang.

2 recordsLinked to original sources

Proposal for a framework for optimizing artificial environments based on physiological feedback.

We propose and then evaluate a new framework for finding the physical parameters of an artificial environment which give rise to given target physiological characteristics. We assume that a human is a system that takes as inputs the physical parameters of an artificial environment and outputs physiological parameters in response. We define our task as the inverse problem; we must find the best inputs from given target outputs. Our proposed framework solves the inverse problem using evolutionary computation techniques to optimize an artificial environment. We evaluate this framework using a simulation with a vibration environment and verify that it works.

Environment, Controlled↗

Improving the performance of predicting users' subjective evaluation characteristics to reduce their fatigue in IEC.

Users' fatigue is the biggest technological hurdle facing Interactive Evolutionary Computation (IEC). This paper introduces the idea of "absolute scale" and "neighbour scale" to improve the performance of predicting users' subjective evaluation characteristics in IEC, and thus it will accelerate EC convergence and reduce users' fatigue. We experimentally evaluate the effect of the proposed method using two benchmark functions. The experimental results show that the convergence speed of IEC using the proposed predictor, which learns from absolute evaluation data, is much faster than the conventional one, which learns from relative data, especially in early generations. Also, IEC with predictors that use recent data are more effective than those which use all past data.

Fatigue↗