  The Traveling Thief Problem is an NP-hard combination of the well known
traveling salesman and knapsack packing problems. In this paper, we use
symbolic regression to learn useful features of near-optimal packing plans,
which we then use to design efficient metaheuristic genetic algorithms for the
traveling thief algorithm. By using symbolic regression again to initialize the
metaheuristic GA with near-optimal individuals, we are able to design a fast,
interpretable, and effective packing initialization scheme. Comparisons against
previous initialization schemes validates our algorithm design.
