Automated Web Service Composition Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Xiao:2012:COMPSACW,

• author = "Liyuan Xiao and Carl K. Chang and Hen-I Yang and Kai-Shin Lu and Hsin-yi Jiang",
• booktitle = "36th Annual IEEE Computer Software and Applications Conference Workshops (COMPSACW 2012)",
• title = "Automated Web Service Composition Using Genetic Programming",
• year = "2012",
• pages = "7--12",
• month = "16-20 " # jul,
• address = "Izmir",
• keywords = "genetic algorithms, genetic programming, genetic improvement, Web services, convergence, graph theory, knowledge based systems, probability, program testing, semantic networks, SDG, atomic Web service, automated Web service composition, black-box testing, business integration, convergence process, convergence rate, input analysis, knowledge rules, mutation operation, output analysis, population quality, probability, semantic meaning, service dependency graph, Complexity theory, Semantics, Sociology, Statistics, Syntactics, Testing, Web services, black-box testing, functional requirements, services composition, test cases",
• isbn13 = "978-1-4673-2714-5",
• DOI = "doi:10.1109/COMPSACW.2012.12",
• size = "6 pages",
• abstract = "Automated web service composition can largely reduce human efforts in business integration. We present an approach to fully automate web service composition without workflow or knowing the semantic meaning of atomic web service. The experiment results show that the accuracy of our composition method using Genetic Programming (GP), in terms of the number of times an expected composition that can be derived versus the total number of runs, can be over 90percent. Based on the traditional GP used in web service composition, our algorithm achieved improvements in three aspects: 1. We do black-box testing on each individual in each population. The success rate of tests is taken into account by the fitness function of GP so that the convergence rate can be faster; 2. We comply with services knowledge rules such as service dependency graph (SDG) when generating individual web service compositions in each population to improve the convergence process and population quality; 3. We choose cross-over or mutation operation based on the parent individuals' input and output analysis instead of by probability as typically done in related work. In this way, GP can generate better children even under the same parents.",
• notes = "Also known as \cite{6341542}",

}

Genetic Programming entries for Liyuan Xiao Carl K Chang Hen-I Yang Kai-Shin Lu Hsin-yi Jiang

Citations