The influence of genetic operators in a Genetic Algorithm used for estimating the energy consumption in a Passive House

Genetic Algorithms are frequently used to estimate parameters in complex mathematical models. However, it is not always straightforward how the parameters of the genetic algorithm should be set in order to find optimal solutions more efficiently. In this paper we will discuss how the genetic operators, mutation and cross over, influence performance within a parameter estimation program which estimates energy consumption in a Passive House. We have varied them independently of one another and have gained insight on the ways they affect our program and how they should be set in order to maximize performance.