The feasible solutions of the traveling salesman problem with pickup and delivery (TSPPD) are represented by vertex lists in existing literature. However, when the TSPPD requires that the loading and unloading operations must be performed in a last-in-first-out (LIFO) manner, we show that its feasible solutions can be represented by trees. Consequently, we develop a variable neighbourhood search (VNS) heuristic for the TSPPD with last-in-first-out loading (TSPPDL) involving several search operators based on the tree data structure. Experiments show that our VNS heuristic is superior to the current best heuristics for TSPPDL in terms of both solution quality and computing time.

Practical optimization problems often have objective functions that cannot be easily calculated. As a result, comparison-based algorithms that solve such problems use comparison functions that are imperfect (i.e. they may make errors). Machine learning algorithms that search for game-playing programs are typically imperfect comparison algorithms. This paper presents M2ICAL, an algorithm analysis tool that uses Monte Carlo simulations to derive a Markov chain model for imperfect comparison algorithms. Once an algorithm designer has modeled an algorithm using M2ICAL as a Markov chain, it can be analyzed using existing Markov chain theory. Information that can be extracted from the Markov chain include the estimated solution quality after a given number of iterations; the standard deviation of the solutions' quality; and the time to convergence.

This paper describes a series of experiments using co-evolution of artificial neural networks on a game called Kalah. The technique employed closely follows the one used by Chellapilla and Fogel to evolve the successful checkers program Anaconda. The experiments aim to provide insight on the effect of including piece differential information, a basic yet crucial piece of expert knowledge, into the neural network inputs.

One of the desirable attributes of real-life examination timetabling solutions is the maximization of paper spread, which is a measure of the amount of study time that each student has between examinations. We make use of the push-forward insertion heuristic (PFIH), a technique commonly employed in the vehicle routing problem, to find timetable solutions that maximize paper spread. This is done by including PFIH as part of a hybrid exam-timetablmg framework known as the Combined Method.

The join relational operation is one of the most expensive among database operations. In this study, we consider the problem of scheduling page accesses in join processing. This raises two interesting problems: 1) determining a page access sequence that uses the minimum number of buffer pages without any page reaccesses, and 2) determining a page access sequence that minimizes the number of page reaccesses for a given buffer size. We use a graph model to represent the pages from the relations that contain tuples to be joined, and present new heuristics for the two problems based on the sort-merge join and the simple TID algorithm. Our experimental results show that the new heuristics perform well.