Proceedings of the seventh international conference (1990) on Machine learning
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
Purposive behavior acquisition for a real robot by vision-based reinforcement learning
Machine Learning - Special issue on robot learning
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
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
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
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Crucial to the more widespread use of evolutionary computation techniques is the ability to scale up to handle complex problems. In the field of genetic programming, a number of decomposition and reuse techniques have been devised to address this. As an alternative to the more commonly employed encapsulation methods, we propose an approach based on the division of test input cases into subsets, each dealt with by an independently evolved code segment. Two program architectures are suggested for this hierarchical approach, and experimentation demonstrates that they offer substantial performance improvements over more established methods. Difficult problems such as even-10 parity are readily solved with small population sizes.