The generic genetic algorithm incorporates with rough set theory – An application of the web services composition

Liang, Wen‐Yau; Huang, Chun‐Che

doi:10.1016/j.eswa.2008.06.084

Cited by 41 publications

(25 citation statements)

References 46 publications

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“…Many studies into Web service concretization based on bio-inspired algorithms are attempting to reduce processing time for a near optimum solution, to improve the quality of solutions, and in particular, to avoid being trapped into local optimization. Some studies have carried out comparisons among different versions of the same type of algorithms, such as comparing an ACO algorithm with a chaos ACO algorithm [46], or comparing an improved GA with a standard GA [18], [32], [33], [48], [49], [50], [51]. Other studies have compared different types of algorithms, such as comparing the combination of an ACO algorithm and a GA with a standard GA [23].…”

Section: Discussionmentioning

confidence: 99%

“…The investigation is required to escape from frequently created illegal individuals by the crossover and mutation operator. [51] C, T, Re, Av, and reputation did not provide did not provide constrained single point crossover, probability was 0.8 the selection of mutation focused on one specific gene of the entire chromosome, probability was 0.001…”

Section: Genetic Algorithmsmentioning

confidence: 99%

“…The study [51] incorporated a GA with the rough set theory, and exemplified by solving the Web service The randomly replacement in mutation operator is not efficient.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

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A Systematic Review of Bio-Inspired Service Concretization

Wang

Shen

2017

IEEE Trans. Serv. Comput.

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In service oriented computing, Web service selection is an important part of Web service composition. The Web service composition is achieved by solving the Web service concretization problem. The literature presents two types of Web service concretization approaches: local optimization approaches and global optimization approaches. There are three types of algorithmic methods in the global optimization approaches: optimal methods, sub-optimal methods, and soft constraints-based methods. The bio-inspired algorithms are sub-optimal methods. This paper will firstly present a hierarchical taxonomy of Web service concretization approaches. Then we conduct a systematic review on the current research of Web service concretization based on three bio-inspired algorithms, namely, ant colony optimization algorithms, genetic algorithms, and particle swarm optimization algorithms. Based on the findings from the systematic review, this paper also discusses the underlying applications of bio-inspired algorithms to the data-intensive service concretization problems. Abstract-In service oriented computing, Web service selection is an important part of Web service composition. The Web service composition is achieved by solving the Web service concretization problem. The literature presents two types of Web service concretization approaches: local optimization approaches and global optimization approaches. There are three types of algorithmic methods in the global optimization approaches: optimal methods, sub-optimal methods, and soft constraints-based methods. The bioinspired algorithms are sub-optimal methods. This paper will first present a hierarchical taxonomy of web service concretization approaches. Then we conduct a systematic review on the current research of Web service concretization based on three bio-inspired algorithms, namely, ant colony optimization algorithms, genetic algorithms, and particle swarm optimization algorithms. Based on the findings from the systematic review, this paper also discusses the underlying applications of bio-inspired algorithms to the dataintensive service concretization problems.

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Section: Discussionmentioning

confidence: 99%

Section: Genetic Algorithmsmentioning

confidence: 99%

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A Systematic Review of Bio-Inspired Service Concretization

Wang

Shen

2017

IEEE Trans. Serv. Comput.

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“…In recent decades, rough sets and fuzzy-rough sets theories have been employed in various application areas such as data mining [4,5,86,[301][302][303], software packages [141,304], web ontology [138,[305][306][307], pattern recognition [24,148,187,[308][309][310], granular computing [38,221,238,251], genetic algorithm [310][311][312][313], prototype selection [145,163], solid transportation [146,314,315], social networks [316][317][318], artificial neural network [92,153,319], remote sensing [320,321], and gene selection [158,[322][323][324]]. An et al [140] analysed a regression algorithm based on fuzzy partition, fuzzy-rough sets, estimation of regression values, and fuzzy approximation for estimating wind speed.…”

Section: Distribution Of Papers Based On Other Application Areasmentioning

confidence: 99%

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

et al. 2017

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Rough set theory has been used extensively in fields of complexity, cognitive sciences, and artificial intelligence, especially in numerous fields such as expert systems, knowledge discovery, information system, inductive reasoning, intelligent systems, data mining, pattern recognition, decision-making, and machine learning. Rough sets models, which have been recently proposed, are developed applying the different fuzzy generalisations. Currently, there is not a systematic literature review and classification of these new generalisations about rough set models. Therefore, in this review study, the attempt is made to provide a comprehensive systematic review of methodologies and applications of recent generalisations discussed in the area of fuzzy-rough set theory. On this subject, the Web of Science database has been chosen to select the relevant papers. Accordingly, the systematic and metaanalysis approach, which is called "PRISMA," has been proposed and the selected articles were classified based on the author and year of publication, author nationalities, application field, type of study, study category, study contribution, and journal in which the articles have appeared. Based on the results of this review, we found that there are many challenging issues related to the different application area of fuzzy-rough set theory which can motivate future research studies.

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“…The subscription to web services for ERP applications has two essential advantages: ease of integration and reduction in costs through the hosted application model [3]. The process of analyzing and selecting services in the Web services composition process however, are more complex than the one of analyzing and selecting parts for a product design [4]. This study proposes a method that makes use of the genetic algorithm (GA) concept and the rough set theory to solve the Web services composition problem.…”

Section: Introductionmentioning

confidence: 99%

The Genetic Algorithm Incorporates with Rough Set Theory – An Application of the CloudERP Composition

Liang¹

2013

IJIET

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Abstract-Cloud computing enables many applications of Web services and rekindles the interest of providing ERP services via the Internet. This paper defines a CloudERP platform on which enterprise customers can select web services and customize a unique ERP system to meet their specific needs. A system prototype was built on the Google App Engine platform to verify the proposed composition process. Based on experimental results from running the prototype, the composition method works effectively and has great potential for supporting a fully functional CloudERP platform.

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The generic genetic algorithm incorporates with rough set theory – An application of the web services composition

Cited by 41 publications

References 46 publications

A Systematic Review of Bio-Inspired Service Concretization

A Systematic Review of Bio-Inspired Service Concretization

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

The Genetic Algorithm Incorporates with Rough Set Theory – An Application of the CloudERP Composition

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