Variable-dimensional optimization with evolutionary algorithms using fixed-length representations

This paper discusses a simple representation of variable-d imensional optimization problems for evolutionary algorithms. Altho ugh it was successfully applied to the optimization of multi-layer optical coating s, it is shown that it intro- duces a unintentional bias into the search process with resp ect to the probability of a dimension being generated by mutation and recombination. In order to ex- amine the impact of the bias, the representation was applied to another variable- dimensional problem, the simultaneous estimation of model orders and model parameters of instances of autoregressive moving average processes (ARMA). The results of the parameter study show that quality of the es timation can be improved by removing the bias.