The document discusses using a genetic algorithm to modify the GNU chess program to create a new chess program that beats the original GNU chess program by a measurable margin over several thousand games. The goal is to treat the task as an optimization problem and use a genetic algorithm to introduce "learning from a mentor" as a reverse engineering method to create an improved chess playing program from the original GNU chess code.

1. Introduction
The goal of the work reported here is to
have a genetic algorithm find a chess program which is
an elementary manipulation of the original GNU-chess
Program but beats the latter by a measurable margin
in an evaluation/competition over several thousand
games. The GNU-chess program, which is intended for
research purposes, written in C and downloadable
from the Free Software Foundation’s website is
used. GNU-chess plays at the chess master level
(blitz rating above 2100, normal rating 2300 depending,
however, on the underlying hardware). In this
work, we understand the task as a simple optimization
problem instance and adapt an engineering perspective
similar to which introduces ”learning from a mentor
” as a reverse engineering method.