Period estimation is an important task in the classification of many variable astrophysical objects .
Here we present GRAPE : Genetic Routine for Astronomical Period Estimation , a genetic algorithm optimised for the processing of survey data with spurious and aliased artefacts .
It uses a Bayesian Generalised Lomb-Scargle ( BGLS ) fitness function designed for use with the Skycam survey conducted at the Liverpool Telescope .
We construct a set of simulated light curves using both regular and Skycam survey cadence with four types of signal : sinusoidal , sawtooth , symmetric eclipsing binary and eccentric eclipsing binary .
We apply GRAPE and a BGLS periodogram to this data and show that the performance of GRAPE is superior to the periodogram on sinusoidal and sawtooth light curves with relative hit rate improvement of 18.2 % and 6.4 % respectively .
The symmetric and eccentric eclipsing binary light curves have similar performance on both methods .
We show the Skycam cadence is sufficient to correctly estimate the period for all of the sinusoidal shape light curves although this degrades with increased non-sinusoidal shape with sawtooth , symmetric binary and eccentric binary light curves down by 20 % , 30 % and 35 % respectively .
The runtime of GRAPE demonstrates that light curves with more than 500-1000 data points achieve similar performance in less computing time .
The GRAPE performance can be matched by a frequency spectrum with an oversampled fine-tuning grid at the cost of almost doubling the runtime .
Finally , we propose improvements which will extend this method to the detection of quasi-periodic signals and the use of multiband light curves .