JCLEC: a Java framework for evolutionary computation

Ventura, Sebastián; Romero, Cristóbal; Zafra, Amelia; Delgado, José A.; Hervás, César

doi:10.1007/s00500-007-0172-0

Cited by 129 publications

(70 citation statements)

References 30 publications

(30 reference statements)

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“…The G3P-MI algorithm is implemented in the JCLEC software [41] and its main parameters (collected from the author's proposal) are shown in Table 1. On the other hand, the coverage and hypothesis kernels require to setup the number of threads per block.…”

Section: Configuration Settingsmentioning

confidence: 99%

Speeding up multiple instance learning classification rules on GPUs

2014

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Multiple instance learning is a challenging task in supervised learning and data mining. However, algorithm performance becomes slow when learning from large-scale and high-dimensional data sets. Graphics processing units (GPUs) are being used for reducing computing time of algorithms. This paper presents an implementation of the G3P-MI algorithm on GPUs for solving multiple instance problems using classification rules. The GPU model proposed is distributable to multiple GPUs, seeking for its scalability across large-scale and high-dimensional data sets. The proposal is compared to the multi-threaded CPU algorithm with SSE parallelism over a series of data sets. Experimental results report that the computation time can be significantly reduced and its scalability improved. Specifically, an speedup of up to 149× can be achieved over the multi-threaded CPU algorithm when using four GPUs, and the rules interpreter achieves great efficiency and runs over 108 billion Genetic Programming operations per second.

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Section: Configuration Settingsmentioning

confidence: 99%

Speeding up multiple instance learning classification rules on GPUs

2014

Self Cite

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“…A. Delgado, and C. Hervas [27] designed a framework that can apply to maximize reusability and availability of evolutionary computation with a minimum effort in web mining. They also implemented a graphical user interface Gen Lab that allows users to configure an algorithm, to execute it interactively and to visualize the results obtained.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Optimal Genetic Model to Investigate the User Usage Behaviour on World Wide Web

R¹,

Kumari²

2013

IJDKP

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“…The evolutionary algorithms have been implemented using the 520 JCLEC framework (Ventura et al, 2007;Ramírez et al, 2015). The experiments were run on a HPC cluster of 8 compute nodes with Rocks cluster 6.1 x64 Linux distribution.…”

Section: Experimental Set-upmentioning

confidence: 99%

Evolutionary composition of QoS-aware web services: A many-objective perspective

Ramrez

Parejo

Romero

et al. 2017

Expert Systems with Applications

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Web service based applications often invoke services provided by thirdparties in their workflow. The Quality of Service (QoS) provided by the invoked supplier can be expressed in terms of the Service Level Agreement specifying the values contracted for particular aspects like cost or throughput, among others.In this scenario, intelligent systems can support the engineer to scrutinise the service market in order to select those candidates that best fit with the expected composition focusing on different QoS aspects. This search problem, a.k.a.QoS-aware web service composition, is characterised by the presence of many diverse QoS properties to be simultaneously optimised from a multi-objective perspective. Nevertheless, as the number of QoS properties considered during the design phase increases and a larger number of decision factors come into play, it becomes more difficult to find the most suitable candidate solutions, so more sophisticated techniques are required to explore and return diverse, competitive alternatives. With this aim, this paper explores the suitability of many-objective evolutionary algorithms for addressing the binding problem of web services on the basis of a real-world benchmark with 9 QoS properties. A complete comparative study demonstrates that these techniques, never before * Corresponding author. Tel.: +34 957 21 26 60Email addresses: aramirez@uco.es (Aurora Ramírez), japarejo@us.es (José Antonio Parejo), jrromero@uco.es (José Raúl Romero), sergiosegura@us.es (Sergio Segura), aruiz@us.es (Antonio Ruiz-Cortés) Preprint submitted to Expert Systems with ApplicationsOctober 31, 2016 applied to this problem, can achieve a better trade-off between all the QoS properties, or even promote specific QoS properties while keeping high values for the rest. In addition, this search process can be performed within a reasonable computational cost, enabling its adoption by intelligent and decision-support systems in the field of service oriented computation.

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JCLEC: a Java framework for evolutionary computation

Cited by 129 publications

References 30 publications

Speeding up multiple instance learning classification rules on GPUs

Speeding up multiple instance learning classification rules on GPUs

An Intelligent Optimal Genetic Model to Investigate the User Usage Behaviour on World Wide Web

Evolutionary composition of QoS-aware web services: A many-objective perspective

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