Techreport,
The Physics behind genetic programming
London South Bank University, SCISM, South Bank University, 103 Borough Road, London SE1 0AA, (2001)
Abstract
the historic scenery of calculus of variations (CV),
one of the central tools of theoretical physics, and
its relationship with genetic programming (GP)
algorithms, a search method with would be considered a
numerical solution for the method of
variations.
Conclusion. This paper establishes a relationship
between the CV and GP as its numerical methods. The
central goal of CV is determining the functional that
attend to some constraints solving fundamentals
differential equations by analytical methods while GP
try to obtain the solution applying genetic operators
in tree coded chromosomes. The differential
displacement in analytical solution assumes the format
of a change into the functional structure through the
application of crossover and mutation operators. The
action integral has its similar in the fitness
function, with in the same way is obtained during all
solution interval time. The initial condition appears
in both approaches defining a realisation of an
intrinsic solution (we termed Cognitive Structure) that
is holistic, i.e., complete and self-contained. It?s a
solution not for one single problem, but for a large
class of similar problems. Under this point of view, we
would divide any problem solution as two different
levels: one for the CS search, and the other to its
adaptation to one realization. A general overview is:
the information available of the problem feeds GP
software, with after some generations obtain the
cognitive structure of the problem, or the best
available at this moment with minimise the fitness
function, i.e., the action for the system. This
structure needs to be adapted to the real conditions of
the system.
