Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Discovery of subroutines in genetic programming
Advances in genetic programming
Simultaneous Discovery of Reusable Detectors and Subroutines Using Genetic Programming
Proceedings of the 5th International Conference on Genetic Algorithms
Genetic Programming And Multi-agent Layered Learning By Reinforcements
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Evolving Modules in Genetic Programming by Subtree Encapsulation
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Layered Learning in Genetic Programming for a Cooperative Robot Soccer Problem
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
An Analysis of Koza's Computational Effort Statistic for Genetic Programming
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Investigating the performance of module acquisition in cartesian genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Layered learning in boolean GP problems
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
A developmental method for growing graphs and circuits
ICES'03 Proceedings of the 5th international conference on Evolvable systems: from biology to hardware
The performance of a selection architecture for genetic programming
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
The relationship between search based software engineering and predictive modeling
Proceedings of the 6th International Conference on Predictive Models in Software Engineering
Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering
| Hi-index | 0.00 |
In an attempt to address the scaling up of genetic programming to handle complex problems, we have proposed a hierarchical approach in which programs are formed from independently evolved code fragments, each of which is responsible for handling a subset of the test input cases. Although this approach offers substantial performance advantages in comparison to more conventional systems, the programs it evolves exhibit some undesirable properties for certain problem domains. We therefore propose the introduction of a selfadaptive mechanism that allows the system dynamically to focus evolutionary effort on the program components most in need. Experimentation reveals that not only does this technique lead to better-behaved programs, it also gives rise to further significant performance improvements.