Fast Range Image Registration by an Asynchronous Adaptive Distributed Differential Evolution

In this paper the application of a general-purpose distributed Differential Evolution algorithm to range image registration is presented. The algorithm is characterized by an asynchronous migration mechanism and by a multi-population recombination information exchange, and is also supplied with adaptive updating schemes for automatically setting the Differential Evolution control parameters. In particular, this algorithm has been employed to tackle the problem of the pair-wise range image registration. Given two images with the first set as the model, the scope is to find the best possible spatial transformation of the second image allowing for 3D reconstruction of the original model. Experimental findings demonstrate the ability of such an adaptive algorithm in finding out efficient image transformations. A comparison of the results with those attained by recently presented evolutionary algorithms show the effectiveness of the proposed approach in terms of both quality and robustness of the reconstructed 3D image, and of computational cost

Accurate estimate of Blood Glucose through Interstitial Glucose by Genetic Programming

Subjects suffering from Type 1 diabetes mellitus need to constantly receive insulin injections. To improve their life quality, a desirable solution is represented by the implementation of an artificial pancreas. In this paper we move a preliminary step towards this goal. Namely, we work at the knowledge base for such a device. One of the main problems is to estimate the Blood Glucose (BG) values, starting from the easily available Interstitial Glucose (IG) ones, and this is the aim of our paper. To face this regression task we avail ourselves of Genetic Programming over a real-world database containing both BG and IG measurements for several subjects suffering from Type 1 diabetes, aiming at finding an explicit relationship between BG and IG values under the form of a mathematical expression. This latter could be the core of the knowledge base part of an artificial pancreas. Experimental comparisons against the state-of-the-art models evidence the quality of the proposed approach

An asynchronous adaptive multi-population model for distributed differential evolution

In this paper a general-purpose asynchronous adaptive multi-population model for distributed Differential Evolution (AsAMP-dDE) algorithm is proposed. The distributed algorithm, following the stepping-stone model, is characterized by an asynchronous mechanism for the migration and for a multipopulation recombination employed to exchange information. The adaptive procedure is based on two steps. Firstly a local performance measure related to the average fitness improvement for each subpopulation is computed. Secondly, a specific updating scheme based on these measures takes place to randomly update the control parameter values. The asynchronous migration mechanism and the adaptive procedure allow reducing the number of control parameters to be set in the distributed model. AsAMP-dDE has been tested on the benchmarks of the CEC2016 real parameter single objective competition without adopting any specific mechanism opportunely tailored for solving such test problems. The results show that this algorithm allows obtaining good performance in most of the investigated benchmarks

Adding Chaos to Differential Evolution for Range Image Registration

This paper presents a method for automatically pair–wise registering range images. Registration is effected adding chaos to a Differential Evolution technique and by applying the Grid Closest Point algorithm to find the best possible transformation of the second image causing 3D reconstruction of the original object. Experimental results show the capability of the method in picking up efficient transformations of images with respect to the classical Differential Evolution. The proposed method offers a good solution to build complete 3D models of objects from 3D scan datasets

Extremal Optimization applied to load balancing in execution of distributed programs

International audienceThe paper describes methods for using Extremal Optimization (EO) for processor load balancing during execution of distributed applications. A load balancing algorithm for clusters of multicore processors is presented and discussed. In this algorithm the EO approach is used to periodically detect the best tasks as candidates for migration and for a guided selection of the best computing nodes to receive the migrating tasks. To decrease the complexity of selection for migration, the embedded EO algorithm assumes a two-step stochastic selection during the solution improvement based on two separate fitness functions. The functions are based on specific models which estimate relations between the programs and the executive hardware. The proposed load balancing algorithm is assessed by experiments with simulated load balancing of distributed program graphs. The algorithm is compared against a greedy fully deterministic approach, a genetic algorithm and an EO-based algorithm with random placement of migrated tasks

Parallel extremal optimization in processor load balancing for distributed applications

International audienceThe paper concerns parallel methods for extremal optimization (EO) applied in processor load balancingin execution of distributed programs. In these methods EO algorithms detect an optimized strategy oftasks migration leading to reduction of program execution time. We use an improved EO algorithmwith guided state changes (EO-GS) that provides parallel search for next solution state during solutionimprovement based on some knowledge of the problem. The search is based on two-step stochasticselection using two fitness functions which account for computation and communication assessment ofmigration targets. Based on the improved EO-GS approach we propose and evaluate several versions ofthe parallelization methods of EO algorithms in the context of processor load balancing. Some of them usethe crossover operation known in genetic algorithms. The quality of the proposed algorithms is evaluatedby experiments with simulated load balancing in execution of distributed programs represented as macrodata flow graphs. Load balancing based on so parallelized improved EO provides better convergence ofthe algorithm, smaller number of task migrations to be done and reduced execution time of applications

Multi-Objective Extremal Optimization in Processor Load Balancing for Distributed Programs

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Distributed Java Programs Initial Mapping Based on Extremal Optimization

11 pagesInternational audienceAn extremal optimization algorithm for initial Java program placement on clusters of Java Virtual Machines (JVMs) is presented. JVMs are implemented on multicore processors working under the ProActive Java execution framework. Java programs are represented as Directed Acyclic Graphs in which tasks correspond to methods of distributed active Java objects that communicate using a RMI mechanism. The presented probabilistic extremal optimization approach is based on the local fitness function composed of two sub-functions in which elimination of delays of task execution after reception of required data and the imbalance of tasks execution in processors are used as heuristics for improvements of extremal optimization solutions. The evolution of an extremal optimization solution is governed by task clustering supported by identification of the dominant path in the graph. The applied task mapping is based on dynamic measurements of current loads of JVMs and inter-JVM communication link bandwidth. The JVM loads are approximated by observation of the average idle time that threads report to the OS. The current link bandwidth is determined by observation of the performed average number of RMI calls per second