Parallel genetic algorithm taxonomy

Nowostawski, Mariusz; Poli, Riccardo

doi:10.1109/kes.1999.820127

Cited by 126 publications

(58 citation statements)

References 2 publications

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“…This skeleton belongs to the family of so-called genetic algorithmic skeletons [25,1], i.e. those patterns of computation inspired by the evolutionary computing field, in which optimization techniques emulate the natural selection process.…”

Section: Global Single Population Genetic Skeletonmentioning

confidence: 99%

Design patterns percolating to parallel programming framework implementation

Aldinucci

Campa

Danelutto

et al. 2013

Int J Parallel Prog

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Structured parallel programming is recognised as a viable and effective means of tackling parallel programming problems. Recently, a set of simple and powerful parallel building blocks (RISC-pb 2 l) has been proposed to support modelling and implementation of parallel frameworks. In this work we demonstrate how that same parallel building block set may be used to implement both general purpose parallel programming abstractions, not usually listed in classical skeleton sets, and more specialized domain specific parallel patterns. We discuss the associated implementation techniques and present experimental evidence of the feasibility and e ciency of the approach.

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Section: Global Single Population Genetic Skeletonmentioning

confidence: 99%

Design patterns percolating to parallel programming framework implementation

Aldinucci

Campa

Danelutto

et al. 2013

Int J Parallel Prog

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“…The reader interested in parallel genetic algorithms can refer to the many reviews that have been conducted on this topic [1,21,77,96]. The parallel execution of evolutionary algorithms is one of the most promising ways for the efficient scaling-up of these algorithms.…”

Section: Parallelization In Evolutionary Computationmentioning

confidence: 99%

Scaling up data mining algorithms: review and taxonomy

García-Pedrajas

Haro-García

2012

Prog Artif Intell

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The overwhelming amount of data that are now available in any field of research poses new problems for data mining and knowledge discovery methods. Due to this huge amount of data, most of the current data mining algorithms are inapplicable to many real-world problems. Data mining algorithms become ineffective when the problem size becomes very large. In many cases, the demands of the algorithm in terms of the running time are very large, and mining methods cannot be applied when the problem grows. This aspect is closely related to the time complexity of the method. A second problem is linked with performance; although the method might be applicable, the size of the search space prevents an efficient execution, and the resulting solutions are unsatisfactory. Two approaches have been used to deal with this problem: scaling up data mining algorithms and data reduction. However, because data reduction is a data mining task itself, this technique also suffers from scalability problems. Thus, for many problems, especially when dealing with very large datasets, the only way to deal with the aforementioned problems is to scale up the data mining algorithm. Many efforts have been made to obtain methods that can be used to scale up existing data mining algorithms. In this paper, we review the methods that have been used to address the problem of scalability. We focus on general ideas, rather than specific implementations, that can be used to provide a general view of the current approaches for scaling up data mining methods. A taxonomy of the algorithms is proposed, and many examples of different tasks are presented. Among the different techniques used for data mining, we will pay special attention to evolutionary methods, because these methods have been used very successfully in many data mining tasks.

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“…In contrast to the presently proposed asynchronous EA, the last two publications rely on generation-based EAs with a shift of emphasis from cluster to grid computing. In the literature, various classifications of PEAs can be found (Alba and Tomassini 2002, Cantú-Paz 1998, Nowostawski and Poli 1999.…”

Section: Vg Asouti and Kc Giannakogloumentioning

confidence: 99%