Analysis the effect of chromosome and generation count on genetic algorithm in construction projects: A case study

Abstract

Resource leveling is utilized to minimize the resource deviation with a uniform distribution of available resources. Herewith the study is to measure the impact attained due to the variation of input parameters such as the number of chromosomes and generations on resource utilization histograms. A good selection of the internal parameters of a genetic algorithm provides rapid and accurate results. We studied the effects of change in a key parameter, namely the number of chromosomes and number of generations, on the optimization speed and reliability. We found that changing one parameter can be compensated for changing another. For this purpose, different combinations of chromosome count and generation count were used to optimize via a genetic algorithm-based model. The network for evaluation of the framed model was structured based on site observation. The change in the input parameters gives greater impact to the resource histogram. To analyse the impact, various combination of input parameters is implied. The quality of the result is measure with an objective function. By using a meta-GA, we found the possible change in attaining optimum results using different chromosome count and generation count.