OPTIMIZATION OF CALIBRATION DATA WITH THE DYNAMIC GENETIC ALGORITHM

Genetic algorithms constitute a set of powerful search heuristics. A modified genetic algorithm was used to optimize calibration data sets. In order to construct an ideal genetic procedure, the diversity in a population is crucial. The idea proposed is to estimate the diversities along two directions, namely the diversity between the chromosomes in a population and the diversity between the alleles in all chromosomes. The newly defined diversity functions are able to describe the procedure of a genetic algorithm in detail and can be used as a feedback for dynamic control of the process in an almost ideal way. The optimization results show that for both short and long runs the dynamic genetic algorithm is superior to the "classical" genetic algorithms and that after optimization not only can the data sets be compacted and refined but also the predictive ability of the calibration model can be improved.