Memetic algorithms are hybrid evolutionary algorithms that combine local optimization with evolutionary search operators. In this paper we describe an instance of this paradigm designed for the correction of illumination inhomogeneities in images. The algorithm uses the gradient information of an error function embedded in the mutation operator. Moreover, the algorithm is a single-solution population algorithm, which makes it computationally light. The fitness function is defined assuming that the image intensity is piecewise constant and that the illumination bias may be approximated by a linear combination of 2D Legendre polynomials. We call the algorithm instantaneous memetic illumination correction (IMIC).