15 2. LITERATURE REVIEW Since the very beginning of the idea conception of OCBA, the world has witnessed incredibly fast development of OCBA, thanks to many researchers who have been diligently working on this topic. With their continual and significant contribution, basic algorithms to effectively allocate computing budget have been developed (Chen, 1995) and further improved to enable people to select the best design among several alternative designs with a limited computing budget (Chen, Lin, Yücesan and Chick, 2000). The OCBA technique has also been extended to solve problems with different objectives but of similar nature, and these problems include the problem of selecting the optimal subset of top designs (Chen. , He, Fu and Lee, 2008), the problem of solving the multi-objective problem by selecting the correct Pareto set with high probability(Chen and Lee, 2009; Lee, Chew, Teng and Goldsman, 2010), the problem of selecting the best design when samples are correlated (Fu, Hu, Chen and Xiong, 2007), the problem of OCBA for constrained optimization (Pujowidianto, Lee, Chen and Yep, 2009), etc. The application of OCBA can be found in various domains, such as in product design (Chen, Donohue, Yücesan and Lin, 2003), air traffic management (Chen and He, 2005), etc. Furthermore, the OCBA technique has been extended to solve large-scale simulation optimization problem by integrating it with many optimization search algorithms (He, Lee, Chen, Fu and Wasserkrug, 2009; Chew, Lee, Teng and Koh, 2009). Last but not least, the OCBA framework has been expanded to solve problems beyond simulation and optimization, such as data envelopment analysis, design of experiment (Hsieh, Chen and Chang, 2007) and rare-event simulation (Chen and Lee, 2011).