abstract = "Automatic protein structure predictors use the notion
of energy to guide the search towards good candidate
structures. The energy functions used by the
state-of-the-art predictors are defined as a linear
combination of several energy terms designed by human
experts. We hypothesised that the energy based guidance
could be more accurate if the terms were combined more
freely. To test this hypothesis, we designed a genetic
programming algorithm to evolve the protein energy
function. Using several different fitness functions we
examined the potential of the evolutionary approach on
a set of candidate structures generated during the
protein structure prediction process. Although our
algorithms were able to improve over the random walk,
the fitness of the best individuals was far from the
optimum. We discuss the shortcomings of our initial
algorithm design and the possible directions for
further research.",