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 on-line, adaptive connectionistbased learning systems called evolving connectionist systems (ECoS). ECoS are systems that evolve their structure and functionality through on-line, adaptive learning from incoming data. The ECoS paradigm is combined here with the paradigm of evolutionary computation to attempt to solve a difficult task of on-line 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.