Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Explicitly defined introns and destructive crossover in genetic programming
Advances in genetic programming
The evolution of size and shape
Advances in genetic programming
Accurate Replication in Genetic Programming
Proceedings of the 6th International Conference on Genetic Algorithms
Complexity Compression and Evolution
Proceedings of the 6th International Conference on Genetic Algorithms
Evolving Compact Solutions in Genetic Programming: A Case Study
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Fitness Causes Bloat: Mutation
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Code growth in genetic programming
Code growth in genetic programming
Balancing accuracy and parsimony in genetic programming
Evolutionary Computation
Code growth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
An adverse interaction between crossover and restricted tree depth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Generality versus size in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
An Experimental Study on Fitness Distributions of Tree Shapes in GP with One-Point Crossover
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Bloat control operators and diversity in genetic programming: A comparative study
Evolutionary Computation
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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.