  Image Registration (IR) is the process of aligning two (or more) images of
the same scene taken at different times, different viewpoints and/or by
different sensors. It is an important, crucial step in various image analysis
tasks where multiple data sources are integrated/fused, in order to extract
high-level information.
  Registration methods usually assume a relevant transformation model for a
given problem domain. The goal is to search for the "optimal" instance of the
transformation model assumed with respect to a similarity measure in question.
  In this paper we present a novel genetic algorithm (GA)-based approach for
IR. Since GA performs effective search in various optimization problems, it
could prove useful also for IR. Indeed, various GAs have been proposed for IR.
However, most of them assume certain constraints, which simplify the
transformation model, restrict the search space or make additional
preprocessing requirements. In contrast, we present a generalized GA-based
solution for an almost fully affine transformation model, which achieves
competitive results without such limitations using a two-phase method and a
multi-objective optimization (MOO) approach.
  We present good results for multiple dataset and demonstrate the robustness
of our method in the presence of noisy data.
