  In this paper, the publicly available dataset of condition based maintenance
of combined diesel-electric and gas (CODLAG) propulsion system for ships has
been utilized to obtain symbolic expressions which could estimate gas turbine
shaft torque and fuel flow using genetic programming (GP) algorithm. The entire
dataset consists of 11934 samples that was divided into training and testing
portions of dataset in an 80:20 ratio. The training dataset used to train the
GP algorithm to obtain symbolic expressions for gas turbine shaft torque and
fuel flow estimation consisted of 9548 samples. The best symbolic expressions
obtained for gas turbine shaft torque and fuel flow estimation were obtained
based on their $R^2$ score generated as a result of the application of the
testing portion of the dataset on the aforementioned symbolic expressions. The
testing portion of the dataset consisted of 2386 samples. The three best
symbolic expressions obtained for gas turbine shaft torque estimation generated
$R^2$ scores of 0.999201, 0.999296, and 0.999374, respectively. The three best
symbolic expressions obtained for fuel flow estimation generated $R^2$ scores
of 0.995495, 0.996465, and 0.996487, respectively.
