1. A method for optimizing medical diagnostic classifiers using a genetic algorithm, the method comprising: training a classifier via a set of learning cases, the learning cases comprising measurement data for a set of measurements acquired from a pool of human test subjects some of whom have cancer and some of whom do not have cancer, the measurement data comprising measured concentrations of organic macromolecules; producing a first generation chromosome population comprising chromosomes, each chromosome represented as chromosomal bit-strings; assigning an index value to each gene of an ordered set of genes, wherein each index indexes a measurement of the set of measurements, and each gene of the ordered set of genes is represented as genetic bit-strings; assigning an ordinal position value to an expressed sub-set-size gene that separates expressed genes from unexpressed genes in the ordered set of genes; generating a fitness criterion that qualifies effectiveness of the expressed genes of each chromosome for identifying the cancer in the set of learning cases, the fitness criterion being evaluated without reference to the unexpressed genes of the chromosome to produce successive generation chromosome populations, wherein computational genetic evolving is performed by a computing system, the computational genetic evolving including: mating pairs of parent chromosomes of the present generation chromosome population to generate offspring chromosomes, for each offspring chromosome, computing a value for the fitness criterion using a classifier defined by the measurements specified by the expressed genes of the offspring chromosome without reference to the unexpressed genes of the offspring chromosome and trained on the set of learning cases, and selecting the next generation chromosome population based on the computed values of the fitness criterion; and selecting a classifier corresponding to a most it chromosome identified by genetic evolving.