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Evolving connectionist systems (ECoSs) are a family of constructive artificial neural network algorithms that were first proposed by Kasabov in 1998, where ldquoevolvingrdquo in this context means ldquochanging over time,rdquo rather than evolving through simulated evolution. A decade on the number of ECoS algorithms and the problems to which they have been applied have multiplied. This paper reviews the current state of the art in the field of ECoS networks via a substantial literature review. It reviews: (1) the motivations for ECoS; (2) the major ECoS algorithms in use; (3) previously existing constructive algorithms that are similar to ECoS; (4) empirical evaluations of ECoS networks over benchmark datasets; and (5) applications of ECoS to real-world problems. The paper ends with some suggestions of future directions of research into ECoS networks.