Noncrossover Dither Creeping Mutation-Based Genetic Algorithm for Pipe Network Optimization

Abstract

A noncrossover dither creeping mutation-based genetic algorithm (CMBGA) for pipe network optimization has been developed and is analyzed in this paper. This CMBGA differs from the classic genetic algorithm (GA) optimization in that it does not utilize the crossover operator; instead, it only uses selection and a proposed dither creeping mutation operator. The creeping mutation rate in the proposed dither creeping mutation operator is randomly generated in a range throughout a GA run, rather than being set to a fixed value. In addition, the dither mutation rate is applied at an individual chromosome level rather than at the generation level. The dither creeping mutation probability is set to take values from a small range that is centered about