Hierarchical self-organization in genetic programming
JP Rosca, DH Ballard - Machine Learning Proceedings 1994, 1994 - Elsevier
This paper presents an approach to automatic discovery of functions in Genetic
Programming. The approach is based on discovery of useful building blocks by analyzing
the evolution trace, generalizing blocks to define new functions, and finally adapting the
problem representation on-the-fly. Adaptating the representation determines a hierarchical
organization of the extended function set which enables a restructuring of the search space
so that solutions can be found more easily. Measures of complexity of solution trees are …
Programming. The approach is based on discovery of useful building blocks by analyzing
the evolution trace, generalizing blocks to define new functions, and finally adapting the
problem representation on-the-fly. Adaptating the representation determines a hierarchical
organization of the extended function set which enables a restructuring of the search space
so that solutions can be found more easily. Measures of complexity of solution trees are …
Showing the best result for this search. See all results
