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Context. Asteroid modeling efforts in the last decade resulted in a comprehensive dataset of almost 400 convex shape models and their rotation states. These efforts already provided deep insight into physical properties of main-belt asteroids or large collisional families. Going into finer detail (e.g., smaller collisional families, asteroids with sizes 20 km) requires knowledge of physical parameters of more objects. Aims. We aim to increase the number of asteroid shape models and rotation states. Such results provide important input for further studies, such as analysis of asteroid physical properties in different populations, including smaller collisional families, thermophysical modeling, and scaling shape models by disk-resolved images, or stellar occultation data. This provides bulk density estimates in combination with known masses, but also constrains theoretical collisional and evolutional models of the solar system. Methods. We use all available disk-integrated optical data (i.e., classical dense-in-time photometry obtained from public databases and through a large collaboration network as well as sparse-in-time individual measurements from a few sky surveys) as input for the convex inversion method, and derive 3D shape models of asteroids together with their rotation periods and orientations of rotation axes. The key ingredient is the support of more that 100 observers who submit their optical data to publicly available databases. Results. We present updated shape models for 36 asteroids, for which mass estimates are currently available in the literature, or for which masses will most likely be determined from their gravitational influence on smaller bodies whose orbital deflections will be observed by the ESA Gaia astrometric mission. Moreover, we also present new shape model determinations for 250 asteroids, including 13 Hungarias and three nearEarth asteroids. The shape model revisions and determinations were enabled by using additional optical data from recent apparitions for shape optimization.

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This article deals with an improvement for genetic programming based on a technique originating from the machine learning field: boosting. In a first part of this paper, we test the improvements offered by boosting on binary problems. Then we propose to deal with regression problems, and propose an algorithm, called GPboost, that keeps closer to the original idea of distribution in Adaboost than what has been done in previous implementation of boosting for genetic programming.

The availability of low cost powerful parallel graphics cards has stimulated the port of Genetic Programming (GP) on Graphics Processing Units (GPUs). Our work focuses on the possibilities offered by Nvidia G80 GPUs when programmed in the CUDA language. We compare two parallelization schemes that evaluate several GP programs in parallel. We show that the fine grain distribution of computations over the elementary processors greatly impacts performances. We also present memory and representation optimizations that further enhance computation speed, up to 2.8 billion GP operations per second. The code has been developed with the well known ECJ library.