We will treat the dependence of water content on time as a linear one between t 0 and t. Arrayed binary representations of model parameters are used as the chromosomes in GA when the parameters have to be optimized. Optimization criterion becomes a fitness measure of the chromosomes. The selection operator chooses those chromosomes that will be allowed to reproduce, and on average the fitter chromosomes produce more offspring than the less fit ones. Crossover exchange subparts of two chromosomes, roughly mimicking biological recombination between two single-chromosome organisms.
Mutation changes the allele values in some genes.
Each iteration of selection, crossover, and mutation is called a generation. Usually hundreds of the generations are needed to produce a chromosome with a good fitness. It uses a tournament selection, in which all chromosomes have equal chances to compete for becoming a parent, but the fitter of any two has larger probability to become a parent. Elitism is allowed which means that the algorithm is forced to allow to the best individuals to become parents in each generation. The crossover is uniform and the allele exchange happens in each bit position.