Discussion of Offspring Generation Method for Interactive Genetic Algorithms with Consideration of Multimodal Preference
The interactive genetic algorithm(iGA) is a method to obtain and predict a user’s preference based on subjective evaluation of users, and it has been applied to many unimodal problems, such as designing clothes or fitting of hearing aids. On the other hand, we are interested in applying iGA to user’s preferences, which can be described as a multimodal problem with equivalent fitness values at the peaks. For example, when iGA is applied to product recommendation on shopping sites, users have several types of preference trends at the same time in product selection. Hence, reflecting all the trends in product presentation leads to increased sales and consumer satisfaction. In this paper, we propose a new offspring generation method that enables efficient search even with multimodal user preferences by introducing clustering of selected individuals and generating offspring from each cluster. Furthermore, we perform a subjective experiment using an experimental iGA system for product recommendation to verify the efficiency of the proposed method. The results confirms that the proposed method enables offspring generation with consideration of multimodal preferences, and there is no negative influence on the performance of preference prediction by iGA.
KeywordsDesign Variable Design Space Product Recommendation Generation Range Multimodal Function
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