Pareto rank learning in multi-objective evolutionary algorithms
Ong, Yew Soon
Tsang, Ivor Wai-Hung
Date of Issue2012
IEEE Congress on Evolutionary Computation (2012 : Brisbane, Australia)
School of Computer Engineering
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.
DRNTU::Engineering::Computer science and engineering
© 2012 IEEE.