Computer Science – Neural and Evolutionary Computing
Scientific paper
2011-10-27
Computer Science
Neural and Evolutionary Computing
Scientific paper
One drawback of evolutionary multiobjective optimization algorithms (EMOA) has traditionally been high computational cost to create an approximation of the Pareto front: number of required objective function evaluations usually grows high. On the other hand, for the decision maker (DM) it may be difficult to select one of the many produced solutions as the final one, especially in the case of more than two objectives. To overcome the above mentioned drawbacks number of EMOA's incorporating the decision makers preference information have been proposed. In this case, it is possible to save objective function evaluations by generating only the part of the front the DM is interested in, thus also narrowing down the pool of possible selections for the final solution. Unfortunately, most of the current EMO approaches utilizing preferences are not very intuitive to use, i.e. they may require tweaking of unintuitive parameters, and it is not always clear what kind of results one can get with given set of parameters. In this study we propose a new approach to visually inspect produced solutions, and to extract preference information from the DM to further guide the search. Our approach is based on intuitive use of dynamic query sliders, which serve as a means to extract preference information and are part of the graphical user interface implemented for the efficient UPS-EMO algorithm.
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