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Machine Learning and Applications, Fourth International Conference on (2011)
Honolulu, Hawaii USA
Dec. 18, 2011 to Dec. 21, 2011
ISBN: 978-0-7695-4607-0
pp: 27-30
This paper explores the multi-objective evolutionary algorithm that can effectively solve a multi-objective problem where an importance of the objective differs each other unlike the conventional problem which concerns each objective evenly. Since such a type of a problem is often found in industrial problems (e.g., logistics network), we propose the biased multi-objective optimization using the reference point (i.e., the factor of strongly concerned). Intensive experiment on the multi-objective knapsack problem had revealed that our proposed method was more superior and had higher diversity than the conventional multi-objective optimization method.
multi-objective optimization, genetic algorithm, logistics

K. Hattori, E. Azuma, H. Sato, T. Shimada and K. Takadama, "The Biased Multi-objective Optimization Using the Reference Point: Toward the Industrial Logistics Network," Machine Learning and Applications, Fourth International Conference on(ICMLA), Honolulu, Hawaii USA, 2011, pp. 27-30.
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