Characterising fitness landscapes using predictive local search

Search space characterisation is a field that strives to define properties of gradients with the general aim of finding the most suitable stochastic algorithms to solve the problems. Diagnostic Optimisation characterises the search landscape while the search progresses. In this work, we have improved Predictive Diagnostic Optimisation to reduce the cost of the local search by introducing a sampling procedure to explore the neighbourhood. The neigbhourhood is created by the swap operator and the sample size recorded during the search is shown to correlate with the known characteristics of the problems.