Genetic Programming (GP) is gradually being accepted as a promising variant of Genetic Algorithm (GA) that evolves dynamic hierarchical structures, often described as programs. In other words GP seemingly holds the key to attain the goal of “automated program generation”. However one of the serious problems of GP lies in the “code growth” or “size problem” that occurs as the structures evolve, leading to excessive pressure on system resources and unsatisfying convergence. Several researchers have addressed the problem. However, absence of a general framework and physical constraints, viz, infinitely large resource requirements have made it difficult to find any generic explanation and hence solution to the problem. This paper surveys the major research works in this direction from a critical angle. Overview of a few other major GP concerns is covered in brief. We conclude with a general discussion on “code growth” and other critical aspects of GP techniques, while attempting to highlight on future research directions to tackle such problems.
|Name||Lecture Notes in Computer Science|
|Publisher||Springer, Berlin, Heidelberg|