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:  Individual Representation 
1. Abstract of Cited Paper (BIB001):  This work it is intended to discover classification rules for diagnosing certain pathologies. These rules are capable of discriminating among 12 different pathologies, whose main symptom is chest pain. In order to discover these rules it was used genetic programming as well as some concepts of data mining, particularly the emphasis on the discovery of comprehensible knowledge. 
2. Abstract of Cited Paper (BIB002):  From the Publisher: ::: Describes a framework, called GGP (Generic Genetic Programming), that integrates GP and ILP based on a formalism of logic grammers. Accelerates the learning speed and/or improves the quality of the knowledge induced.

Literature Review:
The application of standard GP to the classification task is relatively straightforward, as long as all the attributes are numeric. In this case we can include in the function set several kinds of mathematical function appropriate to the application domain and include in the terminal set the predicting attributesand possibly a random-constant generator. Once we apply the functions in the internal nodes of a GP individual to the values of the attributes in the leaf nodes of that individual, the system computes a numerical value that is output at the root node of the tree. Assuming a two-class problem, if this output is greater than a given threshold the system predicts a given class, otherwise the system predicts the other class. In this section, however, we are interested in using GP for discovering highlevel, comprehensible prediction (IF-THEN) rules, rather than just producing a numerical signal in the root node. The first obstacle to be overcome is the closure property of GP. This property means that the output of a node in a GP tree can be used as the input to any parent node in the tree. Note that this property is satisfied in the above case of standard GP applied to numeric data, since, in principle, the number returned by a mathematical function can be used as the input to another mathematical function. (In practice, some mathematical functions have to be slightly modified to satisfy the closure property.) When mining a data set containing a mixture of continuous and categorical (or nominal) attribute values this property is not satisfied by standard GP. Different attributes are associated with different operators/functions. E.g. the condition "Age < 18" is valid, but the condition "Sex < female" is not. Several solutions have been proposed to cope with the closure property of GP, when addressing the classification task. One approach is based on the use of constrained-syntax GP. The key idea is that, for each function available in the function set, the user specifies the type of its arguments and the type of its result BIB001 . Crossover and mutation are then modified to create only valid trees, by respecting the user-defined restrictions on tree syntax. An example is shown in Table1, where, for each row, the second and third columns specify the data type of the input and of the output of the function specified in the first column. Once this kind of specification is available to the GP, the system can generate individuals such as the one shown in Fig. 4 . This figure assumes that Atr1 is a categorical (nominal) attribute, whereas Atr3, Atr5, Atr6 are real-valued attributes. Fig. 4 . Example of a GP individual (representing a rule antecedent) meeting the data type restrictions specified in Table 1 Another approach to constrained-syntax GP consists of having a user-defined, domain-dependent grammar specify the syntax of valid rules. The grammar can also be used to incorporate domain-specific knowledge into the GP system. This approach, discussed in detail in BIB003 , has led to the discovery of interesting knowledge (in the opinion of medical experts) in real-world fracture and scoliosis databases. A different approach for coping with the problem of closure in GP for rule discovery consists of somehow modifying the data being mined -rather than modifying the standard GP algorithm. An example of this approach consists of booleanizing all the attributes being mined and then using logical operators (AND, OR, etc.) in the function set. Hence, the output of any node in the tree will be a boolean value, which can be used as input to any logical operator in the corresponding parent node. Systems based on this approach are described in  , BIB002 .