Multi-modal optimisation using a localised surrogates assisted evolutionary algorithm

There has been a steady growth in interest in niching approaches within the evolutionary computation community, as an increasing number of real world problems are discovered that exhibit multi-modality of varying degrees of intensity (modes). It is often useful to locate and memorise the modes encountered - this is because the optimal decision parameter combinations discovered may not be feasible when moving from a mathematical model emulating the real problem to engineering an actual solution, or the model may be in error in some regions. As such a range of disparate modal solutions is of practical use. This paper investigates the use of a collection of localised surrogate models for niche/mode discovery, and analyses the performance of a novel evolutionary algorithm (EA) which embeds these surrogates into its search process. Results obtained are compared to the published performance of state-of-the-art evolutionary algorithms developed for multi-modal problems. We find that using a collection of localised surrogates not only makes the problem tractable from a model-fitting viewpoint, it also produces competitive results with other EA approaches.