The travelling thief problem (TTP) is a multi-component optimisation problem
involving two interdependent NP-hard components: the travelling salesman
problem (TSP) and the knapsack problem (KP). Recent state-of-the-art TTP
solvers modify the underlying TSP and KP solutions in an iterative and
interleaved fashion. The TSP solution (cyclic tour) is typically changed in a
deterministic way, while changes to the KP solution typically involve a random
search, effectively resulting in a quasi-meandering exploration of the TTP
solution space. Once a plateau is reached, the iterative search of the TTP
solution space is restarted by using a new initial TSP tour. We propose to make
the search more efficient through an adaptive surrogate model (based on a
customised form of Support Vector Regression) that learns the characteristics
of initial TSP tours that lead to good TTP solutions. The model is used to
filter out non-promising initial TSP tours, in effect reducing the amount of
time spent to find a good TTP solution. Experiments on a broad range of
benchmark TTP instances indicate that the proposed approach filters out a
considerable number of non-promising initial tours, at the cost of omitting
only a small number of the best TTP solutions