title: Benchmarking RCGAu on the noiseless BBOB testbed.
abstract: 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 10(5)×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 10(8). Although RCGAu found it difficult in getting a solution with the desired accuracy 10(8) 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.
authors.affiliation: School of Mathematics, Statistics and Computer Science, College of Agriculture, Engineering and Science, University of KwaZulu-Natal, Westville, South Africa ; Department of Computer Sciences, Faculty of Science, University of Lagos, Lagos, Nigeria.
authors.forename: Babatunde A
authors.initials: BA
authors.lastname: Sawyerr
authors.affiliation: School of Mathematics, Statistics and Computer Science, College of Agriculture, Engineering and Science, University of KwaZulu-Natal, Westville, South Africa.
authors.forename: Aderemi O
authors.initials: AO
authors.lastname: Adewumi
authors.affiliation: School of Computational and Applied Mathematics, Faculty of Science and TCSE, Faculty of Engineering and Built Environment, University of the Witwatersrand, Johannesburg, South Africa.
authors.forename: M Montaz
authors.initials: MM
authors.lastname: Ali
mesh_terms: D000465:Algorithms; D019985:Benchmarking; D009622:Noise; D013997:Time Factors