Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A Practical Guide to Usability Testing
A Practical Guide to Usability Testing
Bayesian Networks for Data Mining
Data Mining and Knowledge Discovery
Feature Interaction and Dependencies: Modeling Features for Reengineering a Legacy Product Line
SPLC 2 Proceedings of the Second International Conference on Software Product Lines
Smart Phone and Next Generation Mobile Computing (Morgan Kaufmann Series in Networking (Paperback))
Smart Phone and Next Generation Mobile Computing (Morgan Kaufmann Series in Networking (Paperback))
Proceedings of the 10th international conference on Human computer interaction with mobile devices and services
Cost-benefit factor analysis in e-services using bayesian networks
Expert Systems with Applications: An International Journal
Feature fatigue analysis in product development using Bayesian networks
Expert Systems with Applications: An International Journal
Robust independence testing for constraint-based learning of causal structure
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Journal of Intelligent Manufacturing
Multi objective outbound logistics network design for a manufacturing supply chain
Journal of Intelligent Manufacturing
Multi-objective optimization of facility planning for energy intensive companies
Journal of Intelligent Manufacturing
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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.