Are evolutionary algorithm competitions characterizing landscapes appropriately

Currently, researchers in the field of Evolutionary Algorithms (EAs) are very interested in competitions where new algorithm implementations are evaluated and compared. Usually, EA users perform their algorithm selection by following the results published in these competitions, which are typically focused on average performance measures over benchmark sets. These sets are very complete but the functions within them are usually classified into binary classes according to their separability and modality. Here we show that this binary classification could produce misleading conclusions about the performance of the EAs and, consequently, it is necessary to consider finer grained features so that better conclusions can be obtained about what scenarios are adequate or inappropriate for an EA. In particular, new elements are presented to study separability and modality in more detail than is usually done in the literature. The need for such detail in order to understand why things happen the way they do is made evident over three different EAs.

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