A simple genetic algorithm consists of reproduction, crossover and mutation operators. Reproduction simulates the natural selection by copying the solutions having higher fitness of the higher probability into the next generation. New genetic material is created from the survived solutions with crossover which exchanges the parts of randomly selected parent strings to produce better solutions. Mutation generates random changes to the new solutions in order to prevent the loss of possibly useful gene combinations. Genetics-based machine learning One way to use a genetic algorithm in connection of the medical expert systems is automatic learning of knowledge, i.e., heuristics and information that a human expert uses to solve a problem.
Machine learning is especially useful when knowledge acquisition for some reason is difficult.
Automatic knowledge learning can also be used to speed up the expert system development. We have programmed a machine learning system called Galactica that uses a genetic algorithm to discover rules from databases. The system has been used successfully to find the differential diagnostic rules for female urinary incontinence from case data. In future, we indent to develop an expert system to aid physicians in this difficult diagnostic task. People Our research group consist of experts both of computer science and medicine.