Data Extraction from the World Wide Web is a well known, non solved, and a critical problem when complex information systems are designed. These problems are related to the extraction, management and reuse of the huge amount of Web data available. These data have usually a high heterogeneity, volatility and low quality (i.e. format and content mistakes), so it is quite hard to build realible systems. In this chapter we propose an updated state of the art revision of the problem of Web Data Extraction, and an Evolutionary Computation approach based on Genetic Algorithms and Regular Expressions to the problem of automatically learn software entities. These entities, also called wrappers, will be able to extract some kind of Web data structures from examples.