In this paper, a new approach to grammatical evolution is presented. The aim is to generate complete programs using probabilistic modeling and sampling of (probability) distribution of given grammars. To be exact, probabilistic context free grammars are employed and a modified mapping process is developed to create new individuals from the distribution of grammars. To consider problem structures in the individual generation, conditional dependencies between production rules are incorporated into the mapping process. Experiments confirm that the proposed algorithm is more effective than existing methods.

In this paper, a new evolutionary approach to recommender systems is presented. The aim of this work is to develop a new recommendation method that effectively adapts and immediately responds to the user's preference. To this end, content-based filtering is judiciously utilized in conjunction with interactive evolutionary computation (IEC). Specifically, a fitness-based truncation selection and a feature-wise crossover are devised to make full use of desirable properties of promising items within the IEC framework. Moreover, to efficiently search for proper items, the content-based filtering is modified in cooperation with data grouping. The experimental results demonstrate the effectiveness of the proposed approach, compared with existing methods.