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We propose a new clustering ranking based multiobjective evolutionary algorithm. The algorithm uses decision maker’s preferences to reduce the search space and obtain a final set of preferred Pareto-optimal solutions. A new clustering ranking operator using Hierarchical Clustering on Principle Components (HCPC) and K-means methods is developed. We also develop a new crossover operator. The algorithm is implemented on several problems. The work is still in progress.

International Conference on Advanced Technologies, Computer Engineering and Science
ICATCES

Erdi Dasdemir B. Y. ÖZCAN