POET: an evo-devo method to optimize the weights of a large artificial neural networks

Large search spaces as those of artificial neural networks are difficult to search with machine learning techniques. The large amount of parameters is the main challenge for search techniques that do not exploit correlations expressed as patterns in the parameter space. Evolutionary computation with indirect genotype-phenotype mapping was proposed as a possible solution, but current methods often fail when the space is fractured and presents irregularities. This study employs an evolutionary indirect encoding inspired by developmental biology. Cellular proliferations and deletions of variable size allow for the definition of both regular large areas and small detailed areas in the parameter space. The method is tested on the search of the weights of a neural network for the classification of the MNIST dataset. The results demonstrate that even large networks such as those required for image classification can be effectively automatically designed by the proposed evolutionary developmental method. The combination of real-world problems like vision and classification, evolution and development, endows the proposed method with aspects of particular relevance to artificial life

Flooding scenario for four Italian coastal plains using three relative sea level rise models

<p>The coastal areas of the central Mediterranean Sea are sensitive to climate change and the consequent relative sea level rise. Both phenomena may affect densely urbanized and populated areas, causing severe damages.</p> <p>Our maps show the land-marine flooding projections as effects of the expected relative sea level rise for four Italian coastal plains using (i) IPCC AR5 estimations, based on the IPCC RCP 8.5 emission scenarios and (ii) the Rahmstorf 2007 model. Isostatic and tectonic data were added to the global projections to estimate the relative sea changes expected along the coastline by 2100, as well as sea-flooding. The northern Adriatic map shows the study area, extending for about 5500 km², and is presented at a scale of 1:300,000 with two inset maps at a scale of 1:150,000. The Oristano coastal plain is about 125 km²; the map scale is at 1:60,000 with an inset map scale at 1:33,000. The Cagliari coastal study area extends for 61 km²; the map scale is at 1:60,000 with two inset maps at 1:30,000. The Taranto area extends for 4.2 km² and is represented at a scale map of 1:30,000, while the three inset maps are at a scale of 1:10,000.</p