A Multi-objective Genetic Algorithm Approach for Solving Feature Addition Problem in Feature Fatigue Analysis


Feature fatigue (FF) is used to represent the phenomenon of customer's inconsistent satisfaction with products: customers prefer to choose products with more features and capabilities initially, but after having worked with a product, they become frustrated or dissatisfied with the usability problems caused by too many features. To "defeat" FF, it is essential for designers to decide what features should be added when developing a product to make the product attractive enough and not too hard to use at the same time. In this paper, a feature fatigue multi-objective genetic algorithm (FFMOGA) method is reported for solving the feature addition problem. In the proposed method, fitness functions are established based on Bayesian networks, which can represent the uncertain customer preferences and reflect the relationships among features. The computational experiments on a smart phone case show that the FFMOGA approach can find multiple solutions along the Pareto-optimal frontier for designers to select from, and these obtained solutions have good performance in convergence.