Modeling the role of fixational eye movements in real-world scenes

Our eyes never remain still. Even when we stare at a fixed point, small involuntary movements take place in our eyes in an imperceptible manner. Researchers agree on the presence of three main contributions to eye movements when we fix the gaze: microsaccades, drifts and tremor. These small movements carry the image across the retina stimulating the photoreceptors and thus avoiding fading. Nowadays it is commonly accepted that these movements can improve the discrimination performance of the retina. In this paper, several retina models with and without fixational eye movements were implemented by mean of RetinaStudio tool to test the feasibility of these models to be incorporated in future neuroprostheses. For this purpose each retina model has been stimulated with natural scene images in two experiments. Results are discussed from the point of view of a neuroprosthesis development.

Grouping genetic operators for the delineation of functional areas based on spatial interaction

The delineation of functional economic areas, or market areas, is a problem of high practical relevance, since the delineation of functional sets such as economic areas in the US, Travel-to-Work Areas in the United Kingdom, and their counterparts in other OECD countries are the basis of many statistical operations and policy making decisions at local level. This is a combinatorial optimisation problem defined as the partition of a given set of indivisible spatial units (covering a territory) into regions characterised by being (a) self-contained and (b) cohesive, in terms of spatial interaction data (flows, relationships). Usually, each region must reach a minimum size and self-containment level, and must be continuous. Although these optimisation problems have been typically solved through greedy methods, a recent strand of the literature in this field has been concerned with the use of evolutionary algorithms with ad hoc operators. Although these algorithms have proved to be successful in improving the results of some of the more widely applied official procedures, they are so time consuming that cannot be applied directly to solve real-world problems. In this paper we propose a new set of group-based mutation operators, featuring general operations over disjoint groups, tailored to ensure that all the constraints are respected during the operation to improve efficiency. A comparative analysis of our results with those from previous approaches shows that the proposed algorithm systematically improves them in terms of both quality and processing time, something of crucial relevance since it allows dealing with most large, real-world problems in reasonable time.