Development of Pavement Distress Deterioration Prediction Models for Urban Road Network Using Genetic Programming

Table 4

Architecture of the GP model.

Parameters	Values	Description
Initial population size	Dataset	Dataset-1 is of 16 instances consisting of data gathered in 2012 and 2013.
Function set	+, −, ∗, /, sqrt(), tanh(), pow(x, y), log(), exp(), pow(x, 2), fabs()	Set of functions used
Training percentage	70	—
Selection method	Tournament	—
Tournament size of replacement	3	—
Maximum generations	100000	Maximum number of iterations
Crossover	0.8	Probability of crossover
Mutation	0.04	Probability of mutation
	200	Population size
λ	250	Number of children produced
Fitness functions	R^2	Coefficient of determination
	RMSE	Root mean square error