  In evolutionary algorithms, a preselection operator aims to select the
promising offspring solutions from a candidate offspring set. It is usually
based on the estimated or real objective values of the candidate offspring
solutions. In a sense, the preselection can be treated as a classification
procedure, which classifies the candidate offspring solutions into promising
ones and unpromising ones. Following this idea, we propose a classification
based preselection (CPS) strategy for evolutionary multiobjective optimization.
When applying classification based preselection, an evolutionary algorithm
maintains two external populations (training data set) that consist of some
selected good and bad solutions found so far; then it trains a classifier based
on the training data set in each generation. Finally it uses the classifier to
filter the unpromising candidate offspring solutions and choose a promising one
from the generated candidate offspring set for each parent solution. In such
cases, it is not necessary to estimate or evaluate the objective values of the
candidate offspring solutions. The classification based preselection is applied
to three state-of-the-art multiobjective evolutionary algorithms (MOEAs) and is
empirically studied on two sets of test instances. The experimental results
suggest that classification based preselection can successfully improve the
performance of these MOEAs.
