Genetic programming is a branch of genetic algorithms. The main difference between genetic programming and genetic algorithms is the representation of the solution. Genetic programming creates computer programs in the lisp or scheme computer languages as the solution. Genetic programming is an automatic technique for producing a computer program that solves, or approximately solves, a problem. Genetic programming addresses the challenge of getting a computer to solve a problem without explicitly programming it. This challenge calls for an automatic system whose input is a high-level statement of a problems requirements and whose output is a working program that solves the problem. Genetic programming progressively breeds a population of computer programs over a series of generations by starting with a primordial ooze of thousands of randomly created computer programs and using the Darwinian principle of natural selection, recombination (crossover), mutation, gene duplication, gene deletion, and certain mechanisms of developmental biology. Specifically, genetic programming starts with an initial population of randomly generated computer programs composed of the given primitive functions and terminals. The programs in the population are, in general, of different sizes and shapes. The creation of the initial random population is a blind random search of the space of computer programs composed of the problems available functions and terminals. The aim of Genetic Programming - New Approaches and Successful Applications is to show topical advances in the arena of GP, both the development of new theoretical approaches and the development of applications that have successfully solved different real world problems. The book is mainly aimed at postgraduates, researchers and academics, even though it is hoped that it may be of immense useful to undergraduates who aspire to learn about the leading techniques in genetic programming.