Abstract
Ill-definition, uncertainty and multiple objectives are primary characteristics of real-world decision-making processes. During the initial stages of such processes little knowledge appertaining to the problem at hand may be available. A primary task relates to improving problem definition in terms of variables, constraint and both quantitative and qualitative objectives. The problem space develops with information gained in a dynamical process where optimisation plays a secondary role following the establishment of a well-defined problem domain. The paper speculates upon the role of evolutionary computing, complementary computational intelligence techniques and interactive systems that support such problem definition where multiobjective satisfaction plays a major role.
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Parmee, I.C. (2001). Poor-Definition, Uncertainty, and Human Factors - Satisfying Multiple Objectives in Real-World Decision-Making Environments. In: Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A., Corne, D. (eds) Evolutionary Multi-Criterion Optimization. EMO 2001. Lecture Notes in Computer Science, vol 1993. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44719-9_4
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DOI: https://doi.org/10.1007/3-540-44719-9_4
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