Interactive genetic algorithm with implicit uncertainty evaluation for application in personalized search
Interactive evolutionary algorithms have been used successfully for search and optimization problems. However, user evaluation fatigue and uncertainties greatly restrict their application in complex optimization scenarios. Implicit evaluation techniques can reduce user fatigue effectively, but methods for handling evaluation uncertainties have not yet been explored thoroughly. We study an interactive evolutionary algorithm with uncertain preferences using an implicit evaluation model. First, we design a Gaussian function to define evaluation uncertainties based on interactions. Then, by combining the evaluation uncertainties and the interactions, we calculate the preference weights for each variable, with which a weighted Conditional Preference Net is constructed to provide quantitative depiction of the user's evaluation preference. Using the weighted Net, we estimate the fitness of an individual for the achievement of the evolutionary process. In the iteration, the Net is updated according to newly performed interactions to ensure reliable tracking of the user's preference. We apply a genetic algorithm-based interactive evolutionary algorithm to a personalized search for books. The proposed algorithm accelerates search speed, alleviates user fatigue, and achieves higher rates of satisfying solutions.