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Remote Services for Advanced Problem Optimization
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In this work we present an adaptive parallel methodology to optimize the identification of time series through parametric
models, applying it to the case of sunspot series. We employ high precision computation of system identification algorithms,
and use recursive least squares processing and ARMAX (Autoregressive Moving Average Extensive) parametric modelling. This
methodology could be very useful when the high precision mathematical modelling of dynamic complex systems is required. After
explaining the proposed heuristics and the tuning of its parameters, we show the results we have found for several solar series
using different implementations. Thus, we demonstrate how the result precision improves.
The parallel execution of several populations in evolutionary algorithms has usually given good results. Nevertheless, researchers
have to date drawn conflicting conclusions when using some of the parallel genetic programming models. One aspect of the conflict
is population size, since published GP works do not agree about whether to use large or small populations. This paper presents
an experimental study of a number of common GP test problems. Via our experiments, we discovered that an optimal range of
values exists. This assists us in our choice of population size and in the selection of an appropriate parallel genetic programming
model. Finding efficient parameters helps us to speed up our search for solutions. At the same time, it allows us to locate
features that are common to parallel genetic programming and the classic genetic programming technique.
This paper presents an experimental study of distributed multipopulation genetic programming. Using three well-known benchmark problems and one real-life problem, we discuss the role of the parameters that characterize the evolutionary process of standard panmictic and parallel genetic programming. We find that distributing individuals between subpopulations offers in all cases studied here an advantage both in terms of the quality of solutions and of the computational effort spent, when compared to single populations. We also study the influence of communication patterns such as the communication topology, the number of individuals exchanged and the frequency of exchange on the evolutionary process. We empirically show that the topology does not have a marked influence on the results for the test cases studied here, while the frequency and number of individuals exchanged are related and there exists a suitable range for those parameters which is consistently similar for all the problems studied.
An abstract is not available.
In this paper, we present the results obtained inside the research line SIRVA of the project TRACER. We can summarize these results in two aspects. On the one hand, a client/server Internet system has been developed. This system provides services of image processing via web by means of algorithms implemented in reconfigurable circuits (FPGAs). In this way, we offer a platform open to the scientific community, through a well-prepared communication system, providing the reconfigurable computing advantages where these resources are not physically available. Furthermore, this platform allows the performance comparison between the software and hardware (FPGA) implementation of the same image processing operation. Therefore, users, via the Internet and from anywhere, can check in a practical way the performance improvement produced by the FPGA use (in fact, some of our FPGA-based implementations are even more than 139 times faster than the corresponding software implementation), redounding to a greater diffusion of the FPGA virtues. On the other hand, a web repository of artificial-vision problems has also been created with growing dimensions, including a complete description of each problem, hardware and software solutions, practical results and links of interest. This repository is open to any external collaboration, being very easy to extend it with the description of new artificial-vision problems or adding new languages to existent descriptions.
The sections in this article are1The Problem2Background and Literature3Outline4Displaying the Basic Ideas: Arx Models and the Linear Least Squares Method5Model Structures I: Linear Models6Model Structures Ii: Nonlinear Black-Box Models7General Parameter Estimation Techniques8Special Estimation Techniques for Linear Black-Box Models9Data Quality10Model Validation and Model Selection11Back to Data: The Practical Side of Identification
We present an environment for distributed genetic programming
using MPI. Genetic programming is a stochastic evolutionary learning
methodology that can greatly benefit from parallel/distributed
implementations. We describe the distributed system, as well as a
user-friendly graphical interface to the tool. The usefulness of the
distributed setting is demonstrated by the results obtained to date on
several difficult problems, two of which are described in the text