Population size and quality in genetics-based rule learning from medical data.
Population size and quality are parameters which control the performance of genetic algorithms. We researched these parameters in a genetic-based machine learning system Galactica which was used to discover the differential diagnostic rules for female urinary incontinence from case data. The performance of the system was measured with on-line and off-line criteria. Surprisingly, randomly generated small populations (30 and 70 rules) did not promote the best on-line performance as earlier results suggested. Probable explanation is the lack of diversity in initial populations. The seeding of population with positive learning examples was used to obtain more divergent populations. As expected, the seeding increased the on-line performance of small populations. The results are mainly in accord with the earlier results indicating that large randomly generated populations (150 rules) lead to the better off-line performance. Again, the seeding of small populations was successful producing even the better off-line performance than a large population. In conclusion, the seeding allowed the small populations to converge to good rules in relatively short period of time.