my VU: A Next Generation Recommender System 日团■ Interactive User assigns Fig. 1. Early interactive genetic algorithms function. Crucial for the success of this approach is that the number of candidate solutions that humans must evaluate is kept as low as possible, because baby-sitting for an interactive-evolutionary algorithm is not a task humans volunteer for This problem- the human fitness bottleneck- is addressed by interactive evo- lutionary algorithms of the second generation with the help of a meta-level genetic own in Figure 2 N 日■ Optimize object for a given fitness function fitness function! Fitness function GA Fig. 2. Interactive(meta) genetic algorithms. The interactive evolutionary algorithm at the meta-level breeds fitness functions which are used by a second genetic algorithm at the base -level to generate new can- didate solutions. For each fitness function, only the best solution is presented to the user for evaluation. The user evolves(either implicitely or explicitely) the fitness function which captures his taste or experience or aesthetics. An example of such an interactive algorithm, where the user explicitely manipulates a set of weights which express the relative importance of design factors as structural configuration harmony with the surrounding environment, and slenderness in the design of aes- thetic bridge structures, is presented in Furuta et al. (1996). Biles et al. (1996)carry the concept one step further. In GenJam, an interactive genetic algorithm for breed ing jazz-solos, the user implicitely chooses between fitness functions in the form of neural networks. By choosing a jazz-solo, he increases the fitness of the associated neural network of evolutionary algorithm which has been employed e.g. on the International netic Art II site by John Mount, Scott Neal Reilly and Michael Witbrock(describedmyVU: A Next Generation Recommender System 3 Interactive GA User Population of objects sees breeds assigns fitness Fig. 1. Early interactive genetic algorithms. function. Crucial for the success of this approach is that the number of candidate solutions that humans must evaluate is kept as low as possible, because baby-sitting for an interactive-evolutionary algorithm is not a task humans volunteer for. This problem – the human fitness bottleneck – is addressed by interactive evo￾lutionary algorithms of the second generation with the help of a meta-level genetic algorithm as shown in Figure 2. User sees Population GA Interactive Meta-GA Manipulate fitness function! select best Optimize object for a given fitness function! Fitness function Fig. 2. Interactive (meta) genetic algorithms. The interactive evolutionary algorithm at the meta-level breeds fitness functions which are used by a second genetic algorithm at the base-level to generate new can￾didate solutions. For each fitness function, only the best solution is presented to the user for evaluation. The user evolves (either implicitely or explicitely) the fitness function which captures his taste or experience or aesthetics. An example of such an interactive algorithm, where the user explicitely manipulates a set of weights which express the relative importance of design factors as structural configuration, harmony with the surrounding environment, and slenderness in the design of aes￾thetic bridge structures, is presented in Furuta et al. (1996). Biles et al. (1996) carry the concept one step further. In GenJam, an interactive genetic algorithm for breed￾ing jazz-solos, the user implicitely chooses between fitness functions in the form of neural networks. By choosing a jazz-solo, he increases the fitness of the associated neural network. However, the human fitness bottleneck can be tackled with a different kind of evolutionary algorithm which has been employed e.g. on the International Ge￾netic Art II site by John Mount, Scott Neal Reilly and Michael Witbrock (described