Abstract
In multi-objective decision problems many values must
be assigned, such as the importance of the different
criteria and the values of the alternatives with
respect to subjective criteria. Since these assignments
are approximate, it is very important to analyse the
sensitivity of results when small modifications of the
assignments are made. When solving a multicriteria
decision problem, it is desirable to choose a decision
function that leads to a solution as stable as
possible. Proposed here is a method based on genetic
programming that produces better decision functions
than the commonly used ones. The theoretical
expectations are validated by case studies.
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