An improved genetic algorithm (GA) formulation for pipe network optimi
zation has been developed. The new GA uses variable power scaling of t
he fitness function. The exponent introduced into the fitness function
is increased in magnitude as the GA computer run proceeds. In additio
n to the more commonly used bitwise mutation operator, an adjacency or
creeping mutation operator is introduced. Finally, Gray codes rather
than binary codes are used to represent the set of decision variables
which make up the pipe network design. Results are presented comparing
the performance of the traditional or simple GA formulation and the i
mproved GA formulation for the New York City tunnels problem. The case
study results indicate the improved GA performs significantly better
than the simple GA. In addition, the improved GA performs better than
previously used traditional optimization methods such as linear, dynam
ic, and nonlinear programming methods and an enumerative search method
. The improved GA found a solution for the New York tunnels problem wh
ich is the lowest-cost feasible discrete size solution yet presented i
n the literature.