This chapter addresses the integration of knowledge discovery in databases (KDD) and evolutionary algorithms (EAs), particularly genetic algorithms and genetic programming. First we provide a brief overview of EAs. Then the remaining text is divided into three parts. Section 2 discusses the use of EAs for KDD. The emphasis is on the use of EAs in attribute selection and in the optimization of parameters for other kinds of KDD algorithms (such as decision trees and nearest neighbour algorithms).

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... Section 3 discusses three research problems in the design of an EA for KDD, namely: how to discover comprehensible rules with genetic programming, how to discover surprising (interesting) rules, and how to scale up EAs with parallel processing. Finally, section 4 discusses what the added value of KDD is for EAs. This section includes the remark that generalization performance on a separate test set (unseen during training, or EA run) is a basic principle for evaluating the quality of discovered knowledge, and then suggests that this principle should be followed in other EA applications.