Dynamic optimisation of evolving connectionist system training parameters by pseudo-evolution strategy

The paper presents a method based on evolution strategies that attempts to optimise the training parameters of a class of online, adaptive connectionist-based learning systems called evolving connectionist systems (ECoS). ECoS are systems that evolve their structure and functionality through online, adaptive learning from incoming data. The ECoS paradigm is combined with the paradigm of evolutionary computation to attempt to solve a difficult task of online adaptive adjustment and optimisation of the parameter values of the evolving system. Although the method presented is unsuccessful, some useful information about the properties of the ECoS model is still derived from the work