You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery 
Chapter Title:  Introduction 
1. Abstract of Cited Paper (BIB001):  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 
2. Abstract of Cited Paper (BIB002):  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.

Literature Review:
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.