  The teaching learning-based optimization (TLBO) algorithm has shown
competitive performance in solving numerous real-world optimization problems.
Nevertheless, this algorithm requires better control for exploitation and
exploration to prevent premature convergence (i.e., trapped in local optima),
as well as enhance solution diversity. Thus, this paper proposes a new TLBO
variant based on Mamdani fuzzy inference system, called ATLBO, to permit
adaptive selection of its global and local search operations. In order to
assess its performances, we adopt ATLBO for the mixed strength t-way test
generation problem. Experimental results reveal that ATLBO exhibits competitive
performances against the original TLBO and other meta-heuristic counterparts.
