An Adaptive Genetic Programming Approach to QoS-aware Web Services Composition
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Yu:2013:CECa,
-
article_id = "1168",
-
author = "Yang Yu and Hui Ma and Mengjie Zhang",
-
title = "An Adaptive Genetic Programming Approach to
{QoS}-aware Web Services Composition",
-
booktitle = "2013 IEEE Conference on Evolutionary Computation",
-
volume = "1",
-
year = "2013",
-
month = jun # " 20-23",
-
editor = "Luis Gerardo {de la Fraga}",
-
pages = "1740--1747",
-
address = "Cancun, Mexico",
-
keywords = "genetic algorithms, genetic programming",
-
isbn13 = "978-1-4799-0453-2",
-
DOI = "doi:10.1109/CEC.2013.6557771",
-
abstract = "Web services are software entities that can be
deployed, discovered and invoked in the distributed
environment of the Internet through a set of standards
such as Simple Object Access Protocol (SOAP), Web
Services Description Language (WSDL) and Universal
Description, Discovery and Integration (UDDI). However,
atomic web service can only provide simple
functionality. A range of web services are required to
be incorporated into one composite service in order to
offer value-added and complicated functionality when no
existing web service can be found to satisfy users'
request. In service-oriented architecture (SOA), web
services composition has become an efficient solution
to support business-to-business and enterprise
application integration (EAI). In addition to
functional properties (i.e., inputs and outputs), web
services have non-functional properties called quality
of service (QoS) that encompasses a number of
parameters such as execution cost, response time and
availability. Nowadays with the rapid increase in the
number of available web services, a great number of
services provide overlapping or identical functionality
but vary in QoS attribute values. Due to the huge
search space of the composition problem, a genetic
programming (GP) approach is proposed in this paper,
which aims to produce the desired outputs based on
available inputs, as well as ensure that the composite
service has the optimal QoS value. Furthermore, an
adaptive method is applied to the standard form of GP
in order to avoid low rate of convergence and premature
convergence. A series of experiments have been
conducted to evaluate the proposed approach, and the
results show that the adaptive genetic programming
approach (AGP) has a good performance in finding a
valid solution within low search time and is superior
to the traditional approaches",
-
notes = "Quality of service. CEC 2013 - A joint meeting of the
IEEE, the EPS and the IET.",
- }
Genetic Programming entries for
Yang Yu
Hui Ma
Mengjie Zhang
Citations