Directed fuzzy graph-based surrogate model-assisted interactive genetic algorithms with uncertain individual's fitness
In order to alleviate user fatigue of interactive genetic algorithms with an individual's fuzzy and stochastic fitness, we propose a surrogate model-assisted algorithm by using a directed fuzzy graph to extract user cognition. According to cut-set level and interval dominance probability, we present approaches to construct a directed fuzzy graph of an evolutionary population and calculate an individual's precise fitness based on it. By applying the fuzzy entropy, the chance of data sampling is achieved to obtain reliable samples for training the surrogate model. We adopt a support vector regression machine as the surrogate model, train it using the sampled individuals and their precise fitness, and apply a traditional genetic algorithm to optimize the surrogate model for some generations, providing guided individuals to the user to accelerate the evolution. We quantitatively analyze the performance of the presented algorithm in alleviating user fatigue and increasing more opportunities to look for the satisfactory individuals. Finally, we apply our algorithm to a fashion evolutionary design system to demonstrate its efficiency.