An improved genetic algorithm for continuous and mixed discrete–continuous optimization

Modifications to the standard genetic algorithm through a finetuning strategy, a hillclimbing strategy and the use of independent subpopulations coupled with shuffling are described. The improvements obtained are demonstrated using two optimization problems; a continuous variable rainfall–runoff model calibration and a previously-studied mixed discrete–continuous optimization for cost minimization in pressure vessel manufacture. The use of independent subpopulations and shuffling is found to considerably improve optimizations of the two problems whilst the finetuning and hillclimbing notably improve optimization in the model calibration but not the pressure vessel cost minimization. In the rainfall–runoff modelling the parameter sets obtained by the improved genetic algorithm are more consistent and seem more informative than those obtained with the standard genetic algorithm. With the pressure vessel design problem, lower costs are obtained than in previous studies.