abstract = "Traditional genetic programming uses tree-like data
structure to represent a program. It should be
converted into a Lisp code, or needs a custom-made
virtual machine or interpreter to execute the program
generated. Recently, there is a study on genetic
programming directly using Java bytecode, a practical
intermediate language. It evolves a series of commands
that manipulate stack and registers in the virtual
machine and represents them with a simple list data
structure instead of tree. Evolving the intermediate
language is promising because 1) it is easy to combine
an existing program with an automatically generated
program, 2) there are several available development
tools and environments for the language including
virtual machine, decompiler, optimizer and so on, and
3) incorporating the list data structure into the
evolutionary algorithm is simple and straightforward.
In this research, we propose to evolve bytecode of
Python programming language by stack-based genetic
programming. Python is a flexible and popular
programming language powered by plenty of research
tools. For the evolution, we developed representation
and genetic operations for the Python language. We
report that the proposed method produced successful
Python codes for two regression problems.",
notes = "Also known as \cite{2330899} Distributed at
GECCO-2012.