  Owing to the emergence of large datasets, applying current sequential
wrapper-based feature subset selection (FSS) algorithms increases the
complexity. This limitation motivated us to propose a wrapper for feature
subset selection (FSS) based on parallel and distributed hybrid evolutionary
algorithms (EAs) under the Apache Spark environment. The hybrid EAs are based
on the BDE and Binary Threshold Accepting (BTA), a point-based EA, which is
invoked to enhance the search capability and avoid premature convergence of the
PB-DE. Thus, we designed the hybrid variants (i) parallel binary differential
evolution and threshold accepting (PB-DETA), where DE and TA work in tandem in
every iteration, and (ii) parallel binary threshold accepting and differential
evolution (PB-TADE), where TA and DE work in tandem in every iteration under
the Apache Spark environment. Both PB-DETA and PB-TADE are compared with the
baseline, viz., the parallel version of the binary differential evolution
(PB-DE). All three proposed approaches use logistic regression (LR) to compute
the fitness function, namely, the area under ROC curve (AUC). The effectiveness
of the proposed algorithms is tested over the five large datasets of varying
feature space dimension, taken from cyber security and biology domains. It is
noteworthy that the PB-TADE turned out to be statistically significant compared
to PB-DE and PB-DETA. We reported the speedup analysis, average AUC obtained by
the most repeated feature subset, feature subset with high AUC and least
cardinality.
