Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
A Survey of Intron Research in Genetics
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
A study in program response and the negative effects of introns in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
The benefits of computing with introns
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Duplication of coding segments in genetic programming
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Testing the robustness of the genetic algorithm on the floating building block representation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Convergence Time for the Linkage Learning Genetic Algorithm
Evolutionary Computation
Finding building blocks through eigenstructure adaptation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
| Hi-index | 0.00 |
This paper discusses the use of start expression genes and a modified exchange crossover operator in the linkage learning genetic algorithm (LLGA) that enables the genetic algorithm to learn the linkage of building blocks (BBs) through probabilistic expression (PE). The difficulty that the original LLGA encounters is shown with empirical results. Based on the observation, start expression genes and a modified exchange crossover operator are proposed to enhance the ability of the original LLGA to separate BBs and to improve LLGA's performance on uniformly scaled problems. The effect of the modifications is also presented in the paper.