Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/97348
Title: Pareto rank learning in multi-objective evolutionary algorithms
Authors: Seah, Chun-Wei
Ong, Yew Soon
Tsang, Ivor Wai-Hung
Jiang, Siwei
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Seah, C.-W., Ong, Y.-S., Tsang, I. W., & Jiang, S. (2012). Pareto Rank Learning in Multi-objective Evolutionary Algorithms. 2012 IEEE Congress on Evolutionary Computation (CEC).
Abstract: In this paper, the interest is on cases where assessing the goodness of a solution for the problem is costly or hazardous to construct or extremely computationally intensive to compute. We label such category of problems as “expensive” in the present study. In the context of multi-objective evolutionary optimizations, the challenge amplifies, since multiple criteria assessments, each defined by an “expensive” objective is necessary and it is desirable to obtain the Pareto-optimal solution set under a limited resource budget. To address this issue, we propose a Pareto Rank Learning scheme that predicts the Pareto front rank of the offspring in MOEAs, in place of the “expensive” objectives when assessing the population of solutions. Experimental study on 19 standard multi-objective benchmark test problems concludes that Pareto rank learning enhanced MOEA led to significant speedup over the state-of-the-art NSGA-II, MOEA/D and SPEA2.
URI: https://hdl.handle.net/10356/97348
http://hdl.handle.net/10220/12018
DOI: 10.1109/CEC.2012.6252865
Rights: © 2012 IEEE.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

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