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A Territory Defining Multiobjective Evolutionary Algorithms and Preference Incorporation

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2 Author(s)
Karahan, I. ; Univ. of Illinois, Urbana, IL, USA ; Ko╠łksalan, M.

We have developed a steady-state elitist evolutionary algorithm to approximate the Pareto-optimal frontiers of multiobjective decision making problems. The algorithms define a territory around each individual to prevent crowding in any region. This maintains diversity while facilitating the fast execution of the algorithm. We conducted extensive experiments on a variety of test problems and demonstrated that our algorithm performs well against the leading multiobjective evolutionary algorithms. We also developed a mechanism to incorporate preference information in order to focus on the regions that are appealing to the decision maker. Our experiments show that the algorithm approximates the Pareto-optimal solutions in the desired region very well when we incorporate the preference information.

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Evolutionary Computation, IEEE Transactions on  (Volume:14 ,  Issue: 4 )