Abstract
The goal of multimodal optimisation is to identify multiple desirable optima of a fitness landscape within a single run of an evolutionary algorithm. Typically, one must resort to niching methods to perform this task, and such methods often require the use of a niche radius to distinguish between optima. Typically, this niche radius is difficult to set, leading to suboptimal performance of niching methods on real-world problems. In this paper, local niching methods are used to acquire information about the shape of the fitness landscape during the course of a run. This information is subsequently used during the evolutionary process to adapt the niche radius online. Testing on four benchmark problems indicates that adaptive local niching methods are able to find optimal, or near-optimal, values for the niche radius, as part of the normal evolutionary process.