abstract = "In usual Genetic Programming (GP) schemes, only the
best programs survive from one generation to the next.
This implies that useful code, that might be hidden
inside introns in low fitness individuals, is often
lost. In this paper, we propose a new representation
borrowing from Linear GP (LGP), called PhenoGP, where
solutions are coded as ordered lists of instruction
blocks. The main goal of evolution is then to find the
best ordering of the instruction blocks, with possible
repetitions. When the fitness remains stalled, ignored
instruction blocks, which have a low probability to be
useful, are replaced. Experiments show that PhenoGP
achieve competitive results against standard LGP.",
notes = "Also known as \cite{2330900} Distributed at
GECCO-2012.