In recent years the computing power of graphics cards has increased significantly. Indeed, the growth in the computing power of these graphics cards is now several orders of magnitude greater than the growth in the power of computer processor units. Thus these graphics cards are now beginning to be used by the scientific community as low cost, high performance computing platforms. Traditional genetic programming is a highly computer intensive algorithm but due to its parallel nature it can be distributed over multiple processors to increase the speed of the algorithm considerably. This is not applicable for single processor architectures but graphics cards provide a mechanism for developing a data parallel implementation of genetic programming. In this paper we will describe the technique of general purpose computing using graphics cards and how to extend this technique to genetic programming. We will demonstrate the improvement in the performance of genetic programming on single processor architectures which can be achieved by harnessing the computing power of these next generation graphics cards.

Abstract. Ant Colony Optimisation (ACO) is a well known metaheuristic that has proven successful at solving Travelling Salesman Problems (TSP). However, ACO suffers from two issues; the first is that the technique has significant memory requirements for storing pheromone levels on edges between cities and second, the iterative probabilistic nature of choosing which city to visit next at every step is computationally expensive. This restricts ACO from solving larger TSP instances. This paper will present a methodology for deploying ACO on larger TSP instances by removing the high memory requirements, exploiting parallel CPU hardware and introducing a significant efficiency saving measure. The approach results in greater accuracy and speed. This enables the proposed ACO approach to tackle TSP instances of up to 200K cities within reasonable timescales using a single CPU. Speedups of as much as 1200 fold are achieved by the technique.

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Abstract. This paper is concerned with a dynamic vehicle routing problem. The problem is dynamic in the sense that the time it will take to traverse each edge is uncertain. The problem is expressed as a bi-criterion optimisation with the mutually exclusive aims of minimising both the total mean transit time and the total variance in transit time. In this paper we introduce a hybrid dynamic programming -ant colony optimisation technique to solve this problem. The hybrid technique uses the principles of dynamic programming to first solve simple problems using ACO (routing from each adjacent node to the end node), and then builds on this to eventually provide solutions (i.e. Pareto fronts) for routing between each node in the network and the destination node. However, the hybrid technique updates the pheromone concentrations only along the first edge visited by each ant. As a result it is shown to provide the overall solution in quicker time than an established bi-criterion ACO technique, that is concerned only with routing between the start and destination nodes. Moreover, we show that the new technique both determines more routes on the Pareto front, and results in a 20% increase in solution quality for both the total mean transit time and total variance in transit time criteria. However the main advantage of the technique is that it provides solutions in routing between each node to the destination node. Hence it allows "instantaneous" re-routing subject to dynamic changes within the road network.

Genetic Programming (GP) is a computationally intensive technique which also has a high degree of natural parallelism. Parallel computing architectures have become commonplace especially with regards Graphics Processing Units (GPU). Hence, versions of GP have been implemented that utilise these highly parallel computing platforms enabling significant gains in the computational speed of GP to be achieved. However, recently a two dimensional stack approach to GP using a multicore CPU also demonstrated considerable performance gains. Indeed, performances equivalent to or exceeding that achieved by a GPU were demonstrated. This paper will demonstrate that a similar two dimensional stack approach can also be applied to a GPU based approach to GP to better exploit the underlying technology. Performance gains are achieved over a standard single dimensional stack approach when utilising a GPU. Overall, a peak computational speed of over 55 billion Genetic Programming Operations per Second are observed, a two fold improvement over the best GPU based single dimensional stack approach from the literature.

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Abstract-Reducing costs whilst maintaining passenger satisfaction is an important problem for airports. One area this can be applied is the security lane checks at the airport. However, reducing costs through reducing lane openings typically increases queue length and hence passenger dissatisfaction. This paper demonstrates that evolutionary methods can be used to optimise airport security lane schedules such that passenger dissatisfaction and staffing costs can be minimised. However, it is shown that these schedules typically over-fit the forecasts of passenger arrivals at security such that in actuality significant passenger delays can occur with deviations from the forecast. Consequently, this paper further demonstrates that dynamic evolutionary reoptimisation of these schedules can significantly mitigate this over-fitting problem with much reduced passenger delays.

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