Evolving choice structures for genetic programming

Authors:
Shuaiqiang Wang;Jun Ma;Jiming Liu;Xiaofei Niu
Affiliations:
School of Computer Science and Technology, Shandong University, 250101, Jinan, China and Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong;School of Computer Science and Technology, Shandong University, 250101, Jinan, China;Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong;School of Computer Science and Technology, Shandong Jianzhu University, 250101, Jinan, China
Venue:
Information Processing Letters
Year:
2010

Citing 7
Cited 0

Genetic programming: on the programming of computers by means of natural selection

Genetic programming: on the programming of computers by means of natural selection
Generality and Difficulty in Genetic Programming: Evolving a Sort

Proceedings of the 5th International Conference on Genetic Algorithms
Cartesian Genetic Programming

Proceedings of the European Conference on Genetic Programming
The Push3 execution stack and the evolution of control

GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A comparison of linear genetic programming and neural networks inmedical data mining

IEEE Transactions on Evolutionary Computation
Grammatical evolution

IEEE Transactions on Evolutionary Computation
The Automatic Acquisition, Evolution and Reuse of Modules in Cartesian Genetic Programming

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.89

Visualization

Abstract

It is quite difficult but essential for Genetic Programming (GP) to evolve the choice structures. Traditional approaches usually ignore this issue. They define some ''if-structures'' functions according to their problems by combining ''if-else'' statement, conditional criterions and elemental functions together. Obviously, these if-structure functions depend on the specific problems and thus have much low reusability. Based on this limitation of GP, in this paper we propose a kind of termination criterion in the GP process named ''Combination Termination Criterion'' (CTC). By testing CTC, the choice structures composed of some basic functions independent to the problems can be evolved successfully. Theoretical analysis and experiment results show that our method can evolve the programs with choice structures effectively within an acceptable additional time.