Multiple Classifiers-Assisted Evolutionary Algorithm Based on
Decomposition for High-Dimensional Multi-Objective Problems
- Takumi Sonoda ,
- Masaya Nakata
Abstract
Surrogate-assisted multi-objective evolutionary algorithms have advanced
the field of computationally expensive optimization, but their progress
is often restricted to low-dimensional problems. This manuscript
presents a multiple classifiers-assisted evolutionary algorithm based on
decomposition, which is adapted for high-dimensional expensive problems
in terms of the following two insights. Compared to approximation-based
surrogates, the accuracy of classification-based surrogates is robust
for few high-dimensional training samples. Further, multiple local
classifiers can hedge the risk of over-fitting issues. Accordingly, the
proposed algorithm builds multiple classifiers with support vector
machines on a decomposition-based multi-objective algorithm, wherein
each local classifier is trained for a corresponding scalarization
function. Experimental results statistically confirm that the proposed
algorithm is competitive to the state-of-the-art algorithms and
computationally efficient as well.Dec 2022Published in IEEE Transactions on Evolutionary Computation volume 26 issue 6 on pages 1581-1595. 10.1109/TEVC.2022.3159000