Virtual musicians have become a remarkable phenomenon in the contemporary multimedia arts. However, most of the virtual musicians nowadays have not been endowed with abilities to create their own behaviors, or to perform music with human musicians. In this paper, we firstly create a virtual violinist, who can collaborate with a human pianist to perform chamber music automatically without any intervention. The system incorporates the techniques from various fields, including real-time music tracking, pose estimation, and body movement generation. In our system, the virtual musician's behavior is generated based on the given music audio alone, and such a system results in a low-cost, efficient and scalable way to produce human and virtual musicians' co-performance. The proposed system has been validated in public concerts. Objective quality assessment approaches and possible ways to systematically improve the system are also discussed.

Early development of GAs requires many parameters to be tuned. The tuning process increases the difficulty for inexperienced practitioners. Modern GAs have most of these parameters pre-determined, and therefore recent research concerning parameterless schemes has focused on population size. The techniques developed in this paper are mainly based on Harik and Lobo's work and the exponential population scheme (EPS), which double the population until the solution is satisfactory. In this paper, we modify EPS based on theoretical analyses. Specifically, we propose a new termination criterion and an optimized population multiplier. The experiment results show that our scheme reduces 33.4%, 19.1% and 29.6% number of function evaluations (NFE) on hBOA (the parameter-less hBOA), LT-GOMEA and DSMGA-II respectively when compared to Harik-Lobo scheme, and reduces 28.5%, 4.7% and 11.0% NFE on hBOA, LT-GOMEA and DSMGA-II respectively when compared to EPS. In addition, compared to EPS, our scheme empirically reduces the number of failures when using LT-GOMEA to solve the folded trap and MAX-SAT problems.