Journal
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS
Volume 39, Issue 3, Pages 253-269Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMCC.2008.2012254
Keywords
Connectionism and neural nets; knowledge acquisition; survey
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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 evolving in this context means changing over time, 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. a
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