  The Square Kilometre Array (SKA) is a radio telescope designed to operate
between 70MHz and 10GHz. Due to this large bandwidth, the SKA will be built out
of different collectors, namely antennas and dishes to cover the frequency
range adequately. In order to deal with this bandwidth, innovative feeds and
detectors must be designed and introduced in the initial phases of development.
Moreover, the required level of resolution may only be achieved through a
groundbreaking configuration of dishes and antennas. Due to the large
collecting area and the specifications required for the SKA to deliver the
promised science, the configuration of the dishes and the antennas within
stations is an important question. This research builds on the work done before
by Cohanim et al. (2004), Hassan et al. (2005) and Grigorescu et al. (2009) to
further investigate the applicability of machine learning techniques to
determine the optimum configurations for the collecting elements within the
SKA. This work primarily uses genetic algorithms to search a large space of
optimum layouts. Every genetic step provides a population with candidate
individuals each of which encodes a possible solution. These are randomly
generated or created through the combination of previous encodings. In this
study, a number of fitness functions that rank individuals within a population
of dish configurations are investigated. The UV density, connecting wire length
and power spectra are considered to determine a good dish layout.
