A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery <s> Introduction <s> Background on genetic algorithms, LISP, and genetic programming hierarchical problem-solving introduction to automatically-defined functions - the two-boxes problem problems that straddle the breakeven point for computational effort Boolean parity functions determining the architecture of the program the lawnmower problem the bumblebee problem the increasing benefits of ADFs as problems are scaled up finding an impulse response function artificial ant on the San Mateo trail obstacle-avoiding robot the minesweeper problem automatic discovery of detectors for letter recognition flushes and four-of-a-kinds in a pinochle deck introduction to biochemistry and molecular biology prediction of transmembrane domains in proteins prediction of omega loops in proteins lookahead version of the transmembrane problem evolutionary selection of the architecture of the program evolution of primitives and sufficiency evolutionary selection of terminals evolution of closure simultaneous evolution of architecture, primitive functions, terminals, sufficiency, and closure the role of representation and the lens effect. Appendices: list of special symbols list of special functions list of type fonts default parameters computer implementation annotated bibliography of genetic programming electronic mailing list and public repository. <s> BIB001 </s> A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery <s> Introduction <s> 1 What is Data Mining? 2 Statistical Evaluation for Big Data 3 Preparing the Data 4 Data Reduction 5 Looking for Solutions 6 What's Best for Data Reduction and Mining? 7 Art or Science? Case Studies in Data Mining <s> BIB002 </s> A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery <s> Introduction <s> Thank you for reading genetic algorithms data structures evolution programs. Maybe you have knowledge that, people have look hundreds times for their chosen novels like this genetic algorithms data structures evolution programs, but end up in infectious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some harmful virus inside their laptop. <s> BIB003
The amount of data stored in databases continues to grow fast. Intuitively, this large amount of stored data contains valuable hidden knowledge, which could be used to improve the decision-making process of an organization. For instance, data about previous sales might contain interesting relationships between products and customers. The discovery of such relationships can be very useful to increase the sales of a company. However, the number of human data analysts grows at a much smaller rate than the amount of stored data. Thus, there is a clear need for (semi-)automatic methods for extracting knowledge from data. This need has led to the emergence of a field called data mining and knowledge discovery BIB002 . This is an interdisciplinary field, using methods of several research areas (specially machine learning and statistics) to extract highlevel knowledge from real-world data sets. Data mining is the core step of a broader process, called knowledge discovery in databases, or knowledge discovery, for short. This process includes the application of several preprocessing methods aimed at facilitating the application of the data mining algorithm and postprocessing methods aimed at refining and improving the discovered knowledge. This chapter discusses the use of evolutionary algorithms (EAs), particularly genetic algorithms (GAs)  , BIB003 and genetic programming (GP) BIB001 ,  , in data mining and knowledge discovery. We focus on the data mining task of classification, which is the task addressed by most EAs that extract high-level knowledge from data. In addition, we discuss the use of EAs for performing some preprocessing and postprocessing steps of the knowledge discovery process, focusing on attribute selection and pruning of an ensemble of classifiers. We show how the requirements of data mining and knowledge discovery influence the design of EAs. In particular, we discuss how individual representation, genetic operators and fitness functions have to be adapted for extracting high-level knowledge from data. This chapter is organized as follows. Section 2 presents an overview of data mining and knowledge discovery. Section 3 discusses several aspects of the design of GAs for rule discovery. Section 4 discusses GAs for performing some preprocessing and postprocessing steps of the knowledge discovery process. Section 5 addresses the use of GP in rule discovery. Section 6 addresses the use of GP in the preprocessing phase of the knowledge discovery process. Finally, section 7 presents a discussion that concludes the chapter.