Computer-assisted Music Composition Algorithm Design Dependent on Interactive Genetic Algorithm with Interval Fitness

Computer-assisted music composition refers to computer-assisted music composition with the participation of people. However, there are problems such as style and expression. In this paper, a computer-assisted music composition algorithm based on the interactive genetic algorithm with interval fitness is proposed. A new music prediction model is established by integrating melody units and rhythms into traditional models with only notes or rhythms as units. Moreover, the generated music phrases are optimized by the interactive genetic algorithmphrase. The simulation results suggest that the proposed algorithm can generate music phrases quickly with a certain melody logic that conforms to the personal demand of users using a small data set.

EXTRACTING DESIGN RECOMMENDATIONS FROM INTERACTIVE GENETIC ALGORITHM EXPERIMENTS: APPLICATION TO THE DESIGN OF SOUNDS FOR ELECTRIC VEHICLES

The integration of users' perception in the design process is and important challenge for the optimization of products. This study describes how design recommendations can be drawn, from a perceptual experiment with a panel of subjects using a multi-objective interactive genetic algorithm (IGA). The application concerns the bi-objective optimization of the unpleasantness and the detectability of sounds for electric vehicles (EV). After a description of the experimental protocol for the assessment of the detectability and the unpleasantness of EV sounds (listening test), a set of optimal sounds (Pareto efficient) is defined with an IGA experiment. The analysis of these sounds, based on a probabilistic analysis of the selection process, leads to the definition of design recommendations. A second listening test, involving recommended sounds but also other design proposals, allows an evaluation of the validity of the approach. Results show that the sounds recommended obtain interesting performance, in particular to improve the detectability of EV sounds.