[Seeding Methods for Run Transferable Libraries] Capturing Domain Relevant Functionality through Schematic Manipulation for Genetic Programming

@article{Murphy2007SeedingMF,
  title={[Seeding Methods for Run Transferable Libraries] Capturing Domain Relevant Functionality through Schematic Manipulation for Genetic Programming},
  author={Gearoid Murphy and Conor Ryan and Daniel Howard},
  journal={2007 Frontiers in the Convergence of Bioscience and Information Technologies},
  year={2007},
  pages={769-772}
}
This paper applies a recently developed technique of expression structure analysis and parametric distribution to the generation of functional content relevant to the problem domain. This functional basis set will then be iteratively sampled by a GP system as part of the Run Transferable Libraries process. We introduce a new algorithm for adapting the schematic templates discovered by such an analysis into a family of related functional expressions, differentiated by the number of arguments to… 

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References

SHOWING 1-4 OF 4 REFERENCES
Hierarchical Self-Organization in Genetic programming
Run Transferable Libraries - Learning Functional Bias in Problem Domains
TLDR
It is demonstrated that a system using Run Transferable Libraries can solve a selection of standard benchmarks considerably more quickly than GP with ADFs by building knowledge about a problem.
The Evolutionary Induction of Subroutines
TLDR
A genetic algorithm capable of evolving large programs is described by exploiting two new genetic operators which construct and deconstruct parameterized subroutines which help to solve the scaling problem for a class of genetic problem solving methods.
Evolving Modules in Genetic Programming by Subtree Encapsulation
TLDR
This paper investigates the effect of encapsulating tree-based genetic programming subtrees by representing them as atoms in the terminal set, so that the subtree evaluations can be exploited as terminal data.