Learning Cuckoo Search Strategy for t-way Test Generation
Abdullah B. Nasser, AbdulRahman A. Alsewari, Kamal Z. Zamli · Communications in computer and information science · 2018
The performance of meta-heuristic algorithms highly depends on their exploitation and exploration techniques. In the past 30 years, many meta-heuristic algorithms have been developed which adopts different exploitation and exploration techniques. Several studies reported that the hybrid of meta-heuristics algorithms often perform better than its corresponding original algorithm. This paper presents a new hybrid algorithm; called Learning Cuckoo Search (LCS) strategy based on the integration student phase from Teaching Learning based Optimization (TLBO) Algorithm. To evaluate the developed algorithm, we use the problem of t-way test generation as our case study. The experiment results show that LCS has better performance as compared as to the original Cuckoo Search as well many other existing strategies. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.