Benchmarking projection-based real coded genetic algorithm on BBOB-2013 noiseless function testbed
Babatunde Alade Sawyerr, Aderemi O. Adewumi, M. Montaz Ali · 2013
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 #FEs.