One aspect that most of the current research on evolutionary multiobjective optimization (EMO) often disregards is the fact that the solution of a multiobjective optimization problem (MOP) really involves three stages: measurement, search, and decision making.
Being able to find P true does not completely solve an MOP. The decision maker (DM) still has to choose a single solution out of this set. The process of selecting a single solution is not trivial. In fact, there is a set of methodologies regarding how and when to incorporate decisions from the DM into the search process.
Keywords
- Utility Function
- Pareto Front
- Pareto Optimal Solution
- Aspiration Level
- Multiobjective Optimization Problem
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Rights and permissions
Copyright information
© 2007 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
(2007). Multi-Criteria Decision Making. In: Evolutionary Algorithms for Solving Multi-Objective Problems. Genetic and Evolutionary Computation Series. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-36797-2_9
Download citation
DOI: https://doi.org/10.1007/978-0-387-36797-2_9
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-33254-3
Online ISBN: 978-0-387-36797-2
eBook Packages: Computer ScienceComputer Science (R0)