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Brain Computer Interfaces are an essential technology for the advancement of prosthetic limbs, but current signal acquisition methods are hindered by a number of factors, not least, noise. In this context, Feature Selection is required to choose the important signal features and improve classifier accuracy. Evolutionary algorithms have proven to outperform filtering methods (in terms of accuracy) for Feature Selection. This paper applies a single-point heuristic search method, Iterated Local Search (ILS), and compares it to a genetic algorithm (GA) and a memetic algorithm (MA). It then further attempts to utilise Linkage between features to guide search operators in the algorithms stated. The GA was found to outperform ILS. Counter-intuitively, linkage-guided algorithms resulted in higher classification error rates than their unguided alternatives. Explanations for this are explored.