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Is the island model fault tolerant? in
 GECCO’07: Proceedings of the 2007 GECCO Conference Companion on Genetic and Evolutionary Computation, ACM
, 2007
"... In this paper, we present a study on the fault tolerance nature of the island model when applied to Genetic Algorithms. Parallel and distributed models have been extensively applied to GAs when researchers tackle hard problems. The idea is both to reduce computing time while also improving diversity ..."
Abstract

Cited by 8 (1 self)
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In this paper, we present a study on the fault tolerance nature of the island model when applied to Genetic Algorithms. Parallel and distributed models have been extensively applied to GAs when researchers tackle hard problems. The idea is both to reduce computing time while also improving diversity of populations and therefore quality of solutions. Nevertheless, there are few works dealing with the problem of faults that are usually present when a distributed infrastructure is employed for running the parallel algorithm. This paper studies the behavior of the Island Model when faults appear on a parallel computer or a network of computers. Two benchmark problems have been employed, and good results obtained for each of them allow us to reliably consider Island Model as a fault tolerant parallel algorithm.
Characterizing Fault Tolerance in Genetic Programming
, 2010
"... Evolutionary Algorithms, including Genetic Programming (GP), are frequently employed to solve difficult reallife problems, which can require up to days or months of computation. An approach for reducing the timetosolution is to use parallel computing on distributed platforms. Large such platforms ..."
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Cited by 2 (0 self)
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Evolutionary Algorithms, including Genetic Programming (GP), are frequently employed to solve difficult reallife problems, which can require up to days or months of computation. An approach for reducing the timetosolution is to use parallel computing on distributed platforms. Large such platforms are prone to failures, which can even be commonplace events rather than rare occurrences. Thus, fault tolerance and recovery techniques are typically necessary. The aim of this article is to show the inherent ability of Parallel GP to tolerate failures in distributed platforms without using any faulttolerant technique. This ability is quantified via simulation experiments performed using failure traces from realworld distributed platforms, namely, desktop grids, for two wellknown problems.
Universal Indexing of Arbitrary Similarity Models
"... The increasing amount of available unstructured content together with the growing number of large nonrelational databases put more emphasis on the contentbased retrieval and precisely on the area of similarity searching. Although there exist several indexing methods for efficient querying, not all ..."
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The increasing amount of available unstructured content together with the growing number of large nonrelational databases put more emphasis on the contentbased retrieval and precisely on the area of similarity searching. Although there exist several indexing methods for efficient querying, not all of them are bestsuited for arbitrary similarity models. Having a metric space, we can easily apply metric access methods but for nonmetric models which typically better describe similarities between generally unstructured objects the situation is a little bit more complicated. To address this challenge, we introduce SIMDEX, the universal framework that is capable of finding alternative indexing methods that will serve for efficient yet effective similarity searching for any similarity model. Using trivial or more advanced methods for the incremental exploration of possible indexing techniques, we are able to find alternative methods to the widely used metric space model paradigm. Through experimental evaluations, we validate our approach and show how it outperforms the known indexing methods. 1.
University of Hawai’i at Manoa
"... Evolutionary Algorithms (EAs), and particularly Genetic Programming (GP), are techniques frequently employed to solve difficult reallife problems, which can require up to days or months of computation. One approach to reduce the time to solution is to use parallel computing on distributed platforms ..."
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Evolutionary Algorithms (EAs), and particularly Genetic Programming (GP), are techniques frequently employed to solve difficult reallife problems, which can require up to days or months of computation. One approach to reduce the time to solution is to use parallel computing on distributed platforms. Distributed platforms are prone to failures, and when these platforms are large and/or lowcost, failures are expected events rather than catastrophic exceptions. Therefore, fault tolerance and recovery techniques often become necessary. It turns out that Parallel GP (PGP) applications have an inherent ability to tolerate failures. This ability is quantified via simulation experiments performed using failure traces from realworld distributed platforms, namely, desktop grids (DGs), for two wellknown GP problems. A simple technique is then proposed by which PGP applications can better tolerate the different, and often high, failures rates seen in different platforms. Categories and Subject Descriptors I.2.8 [Artificial intelligence]: Problem solving, control methods and search—heuristic methods.