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The Scientific World Journal
Volume 2015, Article ID 734957, 11 pages
Research Article

Benchmarking RCGAu on the Noiseless BBOB Testbed

1School of Mathematics, Statistics and Computer Science, College of Agriculture, Engineering and Science, University of KwaZulu-Natal, Westville, South Africa
2Department of Computer Sciences, Faculty of Science, University of Lagos, Lagos, Nigeria
3School of Computational and Applied Mathematics, Faculty of Science and TCSE, Faculty of Engineering and Built Environment, University of the Witwatersrand, Johannesburg, South Africa

Received 19 July 2014; Accepted 9 November 2014

Academic Editor: Albert Victoire

Copyright © 2015 Babatunde A. Sawyerr et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


RCGAu is a hybrid real-coded genetic algorithm with “uniform random direction” search mechanism. The uniform random direction search mechanism enhances the local search capability of RCGA. In this paper, RCGAu was tested on the BBOB-2013 noiseless testbed using restarts till a maximum number of function evaluations (#FEs) of 105 × D are reached, where D is the dimension of the function search space. RCGAu was able to solve several test functions in the low search dimensions of 2 and 3 to the desired accuracy of 108. Although RCGAu found it difficult in getting a solution with the desired accuracy 108 for high conditioning and multimodal functions within the specified maximum #FEs, it was able to solve most of the test functions with dimensions up to 40 with lower precisions.