Handling preferences in evolutionary multiobjective optimization: a survey

By Carlos Artemio Coello Coello. Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512), 2000

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Dynamic behavior and vibration control of a tensegrity structure (2010, methodology): “Major approaches to Multi-Criteria Decision Making (MCDM) include multi-attribute utility theory and outranking methods (Coello 2000).”
A Micro-Genetic Algorithm for Multiobjective Optimization (2001, methodology): “Finally, we are also interested in using this approach as a basis to develop a model of incorporation of preferences from the decision maker [4].”
Evolutionary algorithms in control systems engineering: a survey (2002, methodology): “Coello (2000) has undertaken a survey of existing approaches to preference handling within MOEAs.”
Techniques for highly multiobjective optimisation: some nondominated points are better than others (2007, background): “This has long been a successful arm of EMO research [15, 6, 8].”
A fuzzy definition of "optimality" for many-criteria optimization problems (2004, methodology): “In eventual cases of problems where preferences are to be included in the optimization, a good overview of current research on this topic in the EMO community is given in [ 15 ] and [16] and a technique for transforming noncrisp (qualitative) relationships between objectives into quantitative attrib”