Progressively Interactive Evolutionary Multi-Objective Optimization ...
Progressively Interactive Evolutionary Multi-Objective Optimization ...
Progressively Interactive Evolutionary Multi-Objective Optimization ...
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<strong>Progressively</strong> <strong>Interactive</strong> <strong>Evolutionary</strong> <strong>Multi</strong>-objective<br />
<strong>Optimization</strong><br />
Ankur Sinha<br />
Department of Business Technology<br />
P.O. Box 21210, FI-00076<br />
Aalto University School of Economics<br />
Helsinki, Finland<br />
Ankur.Sinha@aalto.fi<br />
Abstract<br />
A complete optimization procedure for a multi-objective problem essentially<br />
comprises of search and decision making. Depending upon how the<br />
search and decision making task is integrated, algorithms can be classified<br />
into various categories. Following ‘a decision making after search’<br />
approach, which is common with evolutionary multi-objective optimization<br />
algorithms, requires to produce all the possible alternatives before a<br />
decision can be taken. This, with the intricacies involved in producing<br />
the entire Pareto-front, is not a wise approach for high objective problems.<br />
Rather, for such kind of problems, the most preferred point on the front<br />
should be the target. In this study we propose and evaluate algorithms<br />
where search and decision making tasks work in tandem and the most<br />
preferred solution is the outcome. For the two tasks to work simultaneously,<br />
an interaction of the decision maker with the algorithm is necessary,<br />
therefore, preference information from the decision maker is accepted periodically<br />
by the algorithm and progress towards the most preferred point<br />
is made.<br />
Two different progressively interactive procedures have been suggested<br />
in the dissertation which can be integrated with any existing evolutionary<br />
multi-objective optimization algorithm to improve its effectiveness in<br />
handling high objective problems by making it capable to accept preference<br />
information at the intermediate steps of the algorithm. A number of