nt Colony Optimisation (ACO) is a constructive metaheuristic that uses an analogue of ant trail pheromones to learn about good features of solutions. ACO implementations are typically tailored in an ad hoc manner to suit particular problems. Critically, the pheromone representation for a particula1· problem is usually chosen intuitively rather than by following any systematic process. In some representations, distinct solutions appea1· multiple times, increasing the effective size of the search space and potentially misleading ants as to the true learned value of those solutions. In this paper, we present a novel system for automatically generating appropriate parsimonious pheromone representations based on the characteristics of the problem model that ensures tmique pheromone representation of solutions. This is the first stage in the development of a generalised ACO system that may be applied to a wide range of problems with little or no modification. However, the system we propose may be used in the development of any problem-specific ACO algorithm.
|Title of host publication||Proceedings of the Australian Conference on Artificial Life: ACAL 2003|
|Editors||Hussein A. Abbass, Janet Wiles|
|Publisher||University of New South Wales|
|Number of pages||15|
|Publication status||Published - 2003|