EA crossover schemes for a MLP channel equaliser
P. Power, F. Sweeney, Colin F. N. Cowan · 2003
This paper presents an evolutionary algorithm (EA) in a form similar to the LMS algorithm, which is applied to MLP learning. The gradient-based update term of the LMS is replaced with the EA non-gradient-based random distancing matrix. This matrix is created to share solution gene information between selected parent chromosomes. The channel equalisation problem is used to compare this algorithm against an EA averaging style operator, which has previously been examined in this area. It is shown that there is an improved learning capability in the MLP filter when the EA updating operators are unrestrained in the range of the gene exchange.