Understanding the machinery of gene regulation to control gene expression
has been one of the main focuses of bioinformaticians for years. We use a
multi-objective genetic algorithm to evolve a specialized version of side effect
machines for degenerate motif discovery. We compare some suggested objectives for the motifs they find, test different multi-objective scoring schemes
and probabilistic models for the background sequence models and report our
results on a synthetic dataset and some biological benchmarking suites. We
conclude with a comparison of our algorithm with some widely used motif discovery algorithms in the literature and suggest future directions for
research in this area