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: GENETIC PROGRAMMING WITH LINEAR REPRESENTATION -- A SURVEY 
Chapter Title:  Prerequisites 
1. Abstract of Cited Paper (BIB001):  Reading Genetic Programming IE Automatic Discovery ofReusable Programs (GPII) in its entirety is not a task for the weak-willed because the book without appendices is about 650 pages. An entire previous book by the same author [1] is devoted to describing Genetic Programming (GP), while this book is a sequel extolling an extension called Automatically Defined Functions (ADFs). The author, John R. Koza, argues that ADFs can be used in conjunction with GP to improve its efficacy on large problems. "An automatically defined function (ADF) is a function (i.e., subroutine, procedure, module) that is dynamically evolved during a run of genetic programming and which may be called by a calling program (e.g., a main program) that is simultaneously being evolved" (p. 1). Dr. Koza recommends adding the ADF technique to the "GP toolkit." The book presents evidence that it is possible to interpret GP with ADFs as performing either a top-down process of problem decomposition or a bottom-up process of representational change to exploit identified regularities. This is stated as Main Point 1. Main Point 2 states that ADFs work by exploiting inherent regularities, symmetries, patterns, modularities, and homogeneities within a problem, though perhaps in ways that are very different from the style of programmers. Main Points 3 to 7 are appropriately qualified statements to the effect that, with a variety of problems, ADFs pay off be-

Literature Review:
Five major preparatory steps 10 must be specified in order to apply a GP technique to a particular problem: (1) the set T of terminals (e.g. the independent variables of the problem, zeroargument functions and random constants) (2) the set F of primitive functions, (3) the fitness measure (for explicitly or implicitly measuring the quality of individuals in the population), (4) certain parameters for controlling the run (5) the termination criterion and the method for designating the result of the run. These preparatory steps are problem-dependent so they must be specified for each particular problem by a human user. In this paper we will use regression and classification problems in order to illustrate the way in which the described GP techniques can be applied. In both cases the problem consists in finding a mathematical expression. The quality of a GP individual, the fitness measure, is usually computed by using a set of fitness cases BIB001 , BIB002 . We consider a problem with n inputs: x 1 , x 2 , . . . x n and one output f . Each fitness case is given as a one-dimensional array of (n + 1) values: