  Learning-to-rank is an applied domain of supervised machine learning. As
feature selection has been found to be effective for improving the accuracy of
learning models in general, it is intriguing to investigate this process for
learning-to-rank domain. In this study, we investigate the use of a popular
meta-heuristic approach called simulated annealing for this task. Under the
general framework of simulated annealing, we explore various neighborhood
selection strategies and temperature cooling schemes. We further introduce a
new hyper-parameter called the progress parameter that can effectively be used
to traverse the search space. Our algorithms are evaluated on five publicly
benchmark datasets of learning-to-rank. For a better validation, we also
compare the simulated annealing-based feature selection algorithm with another
effective meta-heuristic algorithm, namely local beam search. Extensive
experimental results shows the efficacy of our proposed models.
