In this paper, a real-coded genetic algorithm (RCGA) which
incorporates an exploratory search mechanism based on vector
projection termed projection-based RCGA (PRCGA) is
benchmarked on the noisefree BBOB 2013 testbed. It is an
enhanced version of RCGA-P in [22, 23]. The projection operator
incorporated in PRCGA shows promising exploratory
search capability in some problem landscape. PRCGA is
equipped with a multiple independent restart mechanism
and a stagnation alleviation mechanism. The maximum
number of function evaluations (#FEs) for each test run
is set to 105 times the problem dimension. PRCGA shows
encouraging results on several problems in the low and moderate
search dimensions. It is able to solve each type of problem
with the dimension up to 40 with lower precision but
not all the functions to the desired level of accuracy of 10
−8
especially for high conditioning and multi-modal functions
within the specified maximum