Black-box optimization benchmarking for noiseless function testbed using a direction-based rcga

This paper benchmarks a novel and efficient real-coded ge-netic algorithm (RCGA) enhanced from our previous work [1] on the noisefree BBOB 2012 testbed. The enhanced al-gorithm termed as direction-based RCGA (DBRCGA) uses relative fitness information to direct the crossover toward a direction that significantly improves the objective fitness. As a base of performance evaluation and comparisons, the max-imum number of function evaluations (#FEs) for each test run is set to 105 times to the problem dimension. Extensive benchmarking test results reveal that all functions can be solved by DBRCGA in the low search dimensions. Although the DBRCGA shows the difficulty in getting a solution with the desired accuracy 10−8 for high conditioning and multi-modal functions within the specified maximum #FEs, the DBRCGA presents good performance in separable function and functions with low or moderate conditioning