Medicine is an important and challenging area for machine learning and computational intelligence approaches. Evolutionary algorithms, in particular, offer a number of advantages when solving problems in this domain. For instance, unlike decision trees and neural-network-based approaches, they are not tied to particular solution representations. This flexibility is important for problems where the aim is to model specific biological or pathological processes, and it also provides scope for using interpretable models, in which evolved solutions can be mined for information. Another important advantage is the ability of evolutionary algorithms to explore a relatively large space of solutions, without requiring
knowledge of the structure of a problem. This means that evolutionary algorithms can often be used as a discovery tool, providing new fundamental knowledge to clinicians. For these reasons and others, over the last decade there has been growing interest in the use of evolutionary algorithms in medicine (Smith and Cagnoni, 2011).