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BACKGROUND Many children with specific language impairment (SLI) exhibit sentence comprehension difficulties. In some instances, these difficulties appear to be related to poor linguistic knowledge and, in other instances, to inferior general processing abilities. Two processing deficiencies evidenced by these children include reduced linguistic processing(More)
Many animals use chemical substances known as pheromones to induce behavioural changes in other members of the same species. The use of pheromones by ants in particular has lead to the development of a number of computational analogues of ant colony behaviour including Ant Colony Optimisation. Although many animals use a range of pheromones in their(More)
—In the commonly used DE/rand/1 variant of differential evolution the primary mechanism of generating new solutions is the perturbation of a randomly selected point by a difference vector. The newly selected point may, if good enough, then replace a solution from the current generation. As the magnitude of difference vectors diminishes as the population(More)
Ant colony optimization (ACO) is a constructive metaheuristic that uses an analogue of ant trail pheromones to learn about good features of solutions. Critically, the pheromone representation for a particular problem is usually chosen intuitively rather than by following any systematic process. In some representations, distinct solutions appear multiple(More)
Constructive metaheuristics explore a tree of constructive decisions, the topology of which is determined by the way solutions are represented and constructed. Some solution representations allow particular solutions to be reached on a greater number of paths in this construction tree than other solutions, which can introduce a bias to the search. However,(More)
Ant colony optimization techniques are usually guided by pheromone and heuristic cost information when choosing the next element to add to a solution. However, while an individual element may be attractive, usually its long term consequences are neither known nor considered. For instance, a short link in a traveling salesman problem may be incorporated into(More)
Each particle of a swarm maintains its current location and its personal best location. It is useful to think of these personal best locations as a population of attractors. When this population of attractors converges, the explorative capacity of the swarm is reduced. The convergence of attractors can occur quickly since the personal best of a particle is(More)
Standard particle swarm optimization cannot guarantee convergence to the global optimum in multi-modal search spaces, so multiple swarms can be useful. The multiple swarms all need initial positions and initial velocities for their particles. Several simple strategies to select initial positions and initial velocities are presented. A series of experiments(More)
When using a constructive search algorithm, solutions to scheduling problems such as the job shop and open shop scheduling problems are typically represented as permutations of the operations to be scheduled. The combination of this representation and the use of a constructive algorithm introduces a bias typically favouring good solutions. When ant colony(More)
—Common explanations of DE's search behaviour as its crossover rate Cr is varied focus on the directionality of the search, as low values make moves aligned with a small number of axes while high values search at angles to the axes. While the direction of search is important, an analysis of moves generated by mutating differing numbers of dimensions(More)