abstract = "The Probabilistic Adaptive Mapping Developmental
Genetic Programming (PAM DGP) algorithm that
cooperatively Co-evolves a population of adaptive
mappings and associated genotypes is used to learn
recursive solutions given a function set consisting of
general (not implicitly recursive) machine-language
instructions. PAM DGP using redundant encodings to
model the evolution of the biological genetic code is
found to more efficiently learn 2nd and 3rd order
recursive Fibonacci functions than related
developmental systems and traditional linear GP. PAM
DGP using redundant encoding is also demonstrated to
produce the semantically highest quality solutions for
all three recursive functions considered (Factorial,
2nd and 3rd order Fibonacci). PAM DGP is then shown to
have produced such solutions by evolving redundant
mappings to select and emphasise appropriate subsets of
the function set useful for producing the naturally
recursive solutions.",
notes = "GECCO-2007 A joint meeting of the sixteenth
international conference on genetic algorithms
(ICGA-2007) and the twelfth annual genetic programming
conference (GP-2007).