Pareto Rank Learning in Multi-objective Evolutionary Algorithms   [ME] [MEA] [EA]

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Seah, C.-W., Ong, Y.-S., Tsang, I., W. and Jiang, S.

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Info: Proceedings of the 2012 IEEE Congress on Evolutionary Computation (Conference proceedings), 2012, p. 2613-2620
Keywords:Evolutionary Computation in Dynamic, Uncertain Environments
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 [ME] optimisations, 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 proposed a Pareto Rank Learning scheme that predicts the Pareto front rank [PF] 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 [TP] concludes that Pareto rank learning enhanced MOEA led to significant speedup over the state-of-the-art NSGA-II, MOEA/D and SPEA2.
Notes:
WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.
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BibTex:
@InProceedings{Seah:2012:CEC,
  title     = {{Pareto} Rank Learning in Multi-objective Evolutionary Algorithms},
  author    = {Chun-Wei Seah and Yew-Soon Ong and Ivor W. Tsang and Siwei Jiang},
  pages     = {2613--2620},
  booktitle = {Proceedings of the 2012 IEEE Congress on Evolutionary Computation},
  year      = {2012},
  editor    = {Xiaodong Li},
  month     = {10-15 June},
  url       = {},
  doi       = {},
  size      = {},
  address   = {Brisbane, Australia},
  ISBN      = {0-7803-8515-2},
  keywords  = {Evolutionary Computation in Dynamic and Uncertain Environments},
  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 optimisations, 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 proposed 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.
},
  notes = {WCCI 2012.
  CEC 2012 - A joint meeting of the IEEE, the EPS and the IET.},
}