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Heuristic evolution with genetic programming for traveling thief problem

机译：遗传规划启发式进化求解旅行小偷问题

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In many real-world applications, one needs to deal with a large multi-silo problem with interdependent silos. In order to investigate the interdependency between silos (sub-problems), the Traveling Thief Problem (TTP) was designed as a benchmark problem. TTP is a combination of two well-known sub-problems, Traveling Salesman Problem (TSP) and Knapsack Problem (KP). Although each sub-problem has been intensively investigated, the interdependent combination has been demonstrated to be challenging, and cannot be solved by simply solving the sub-problems separately. The Two-Stage Memetic Algorithm (TSMA) is an effective approach that has decent solution quality and scalability, which consists of a tour improvement stage and an item picking stage. Unlike the traditional TSP local search operators adopted in the former stage, the heuristic for the latter stage is rather intuitive. To further investigate the effect of item picking heuristic, Genetic Programming (GP) is employed to evolve a gain function and a picking function, respectively. The resultant two heuristics were tested on some representative TTP instances, and showed competitive performance, which indicates the potential of evolving more promising heuristics for solving TTP more systematically by GP.
机译：在许多实际应用中，需要使用相互依赖的孤岛来处理一个大型的多孤岛问题。为了研究筒仓（子问题）之间的相互依赖性，将旅行小偷问题（TTP）设计为基准问题。 TTP是两个著名的子问题的组合，即旅行商问题（TSP）和背包问题（KP）。尽管已经深入研究了每个子问题，但是已经证明相互依赖的组合具有挑战性，并且不能通过简单地单独解决子问题来解决。两阶段模因算法（TSMA）是一种有效的方法，具有不错的解决方案质量和可伸缩性，它由巡回改进阶段和项目挑选阶段组成。与前阶段采用的传统TSP本地搜索运算符不同，后阶段的启发式方法非常直观。为了进一步研究项目拣选启发式的效果，采用遗传编程（GP）分别演化了增益函数和拣选函数。在某些具有代表性的TTP实例上测试了由此产生的两种启发式方法，并显示出竞争性能，这表明有可能发展出更多有希望的启发式方法来由GP更系统地解决TTP。

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