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Knowledge discovery is a new discipline that applies machine learning techniques to large real-world databases to extract knowledge from the data. This knowledge is often expressed as rules modeling the data. Genetic algorithms (GA) are a unique method for evolving high quality solutions from a potentially huge search space of possible solutions. This technique uses a simulated process of natural selection rather than a simulated reasoning process. Genetic algorithms are uniquely suited to data mining problems due to the inductive nature of the problem. This paper describes two GA-based data mining systems, GA-MINIR, a pure GA technique, and DOGMA, a hybrid technique, using GAs to improve on rules generated by another classifier. It also discusses the difficulty of formally analyzing a genetic algorithm in order to compare it with more conventional methods of solving the same problem.
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