New issue of SIGEVOlution is out

A new issue of the SIGEVOlution newsletter is now out (volume 4, issue 3). SIGEVOlution is the newsletter of ACM SIGEVO, the ACM Special Interest Group on Genetic and Evolutionary Computation.

A new issue of the SIGEVOlution newsletter is now out (volume 4, issue 3). SIGEVOlution is the newsletter of ACM SIGEVO, the ACM Special Interest Group on Genetic and Evolutionary Computation.

Recent progress in natural computation and knowledge discovery: an ICNC’09-FSKD’09 special issue

Recent progress in natural computation and knowledge discovery: an ICNC’09-FSKD’09 special issue
Content Type Journal ArticlePages 1-2DOI 10.1007/s00500-010-0559-1Authors
Haiying Wang, University of Ulster School of Computing and Mathematics and…

Recent progress in natural computation and knowledge discovery: an ICNC’09-FSKD’09 special issue

  • Content Type Journal Article
  • Pages 1-2
  • DOI 10.1007/s00500-010-0559-1
  • Authors
    • Haiying Wang, University of Ulster School of Computing and Mathematics and Computer Science Research Institute Shore Road Newtownabbey Co. Antrim BT37 0QB UK
    • Yixin Chen, Washington University in St Louis Computer Science and Engineering Campus Box 1045, One Brookings Drive St. Louis MO 63130 USA
    • Hepu Deng, RMIT University Business Information Technology GPO Box 2476 Melbourne VIC 3001 Australia
    • Lipo Wang, Nanyang Technological University School of Electrical and Electronic Engineering Block S1, 50 Nanyang Avenue Singapore 639798 Singapore

A novel multi-population cultural algorithm adopting knowledge migration

Abstract  In existing multi-population cultural algorithms, information is exchanged among sub-populations by individuals. However,
migrated individuals cannot reflect enough evolutionary information, which limits the evolution performance. …

Abstract  

In existing multi-population cultural algorithms, information is exchanged among sub-populations by individuals. However,
migrated individuals cannot reflect enough evolutionary information, which limits the evolution performance. In order to enhance
the migration efficiency, a novel multi-population cultural algorithm adopting knowledge migration is proposed. Implicit knowledge
extracted from the evolution process of each sub-population directly reflects the information about dominant search space.
By migrating knowledge among sub-populations at the constant intervals, the algorithm realizes more effective interaction
with less communication cost. Taken benchmark functions with high-dimension as the examples, simulation results indicate that
the algorithm can effectively improve the speed of convergence and overcome premature convergence.

  • Content Type Journal Article
  • Pages 1-9
  • DOI 10.1007/s00500-010-0556-4
  • Authors
    • Yi-nan Guo, College of Information and Electronic Engineering, China University of Mining and Technology, Xuzhou, 221008 Jiangsu China
    • Jian Cheng, College of Information and Electronic Engineering, China University of Mining and Technology, Xuzhou, 221008 Jiangsu China
    • Yuan-yuan Cao, College of Information and Electronic Engineering, China University of Mining and Technology, Xuzhou, 221008 Jiangsu China
    • Yong Lin, College of Information and Electronic Engineering, China University of Mining and Technology, Xuzhou, 221008 Jiangsu China

A novel approach to annotating web service based on interface concept mapping and semantic expansion

Abstract  With the rapid development of web service technology in these years, traditional standards have been matured during the process
of service registry and discovery. However, it is difficult for service requesters to discover satisfac…

Abstract  

With the rapid development of web service technology in these years, traditional standards have been matured during the process
of service registry and discovery. However, it is difficult for service requesters to discover satisfactory web services.
The reason for this phenomenon is that the traditional service organization mode lacks semantic understanding ability for
service function interface. This paper proposes a novel approach to annotating web services. We first adopt domain ontology
as a semantic context, and give our general framework of service semantic annotation. Then, interface concept mapping algorithm
and service interface expansion algorithm are respectively presented in detail. Finally, the generation process of semantic
web service repository is presented based on preceding algorithms. Simulation experiment results demonstrate that annotated
web services by the proposed method can more satisfy requirements for service requesters than traditional ones by service
matchmaking engine. It can get better service discovery effectiveness.

  • Content Type Journal Article
  • Pages 1-10
  • DOI 10.1007/s00500-010-0548-4
  • Authors
    • Guobing Zou, Tongji University Department of Computer Science and Technology Shanghai 201804 China
    • Yang Xiang, Tongji University Department of Computer Science and Technology Shanghai 201804 China
    • Yanglan Gan, Tongji University Department of Computer Science and Technology Shanghai 201804 China
    • Yixin Chen, Washington University Department of Computer Science and Engineering St. Louis MO 63130 USA

Foreword

Foreword
Content Type Journal ArticleDOI 10.1007/s11047-010-9181-5Authors
Friedrich Simmel, Technische Universität München Garching GermanyAshish Goel, Stanford University Stanford CA USA

Journal Natural ComputingOnline ISSN 1572-9796Print …

Foreword

  • Content Type Journal Article
  • DOI 10.1007/s11047-010-9181-5
  • Authors
    • Friedrich Simmel, Technische Universität München Garching Germany
    • Ashish Goel, Stanford University Stanford CA USA

Using selfish gene theory to construct mutual information and entropy based clusters for bivariate optimizations

Abstract  This paper proposes a new approach named SGMIEC in the field of estimation of distribution algorithm (EDA). While the current
EDAs require much time in the statistical learning process as the relationships among the variables are t…

Abstract  This paper proposes a new approach named SGMIEC in the field of estimation of distribution algorithm (EDA). While the current
EDAs require much time in the statistical learning process as the relationships among the variables are too complicated, the
selfish gene theory (SG) is deployed in this approach and a mutual information and entropy based cluster (MIEC) model with
an incremental learning and resample scheme is also set to optimize the probability distribution of the virtual population.
Experimental results on several benchmark problems demonstrate that, compared with BMDA, COMIT and MIMIC, SGMIEC often performs
better in convergent reliability, convergent velocity and convergent process.

  • Content Type Journal Article
  • Pages 1-9
  • DOI 10.1007/s00500-010-0557-3
  • Authors
    • Feng Wang, Wuhan University State Key Laboratory of Software Engineering Wuhan China
    • Zhiyi Lin, Wuhan University State Key Laboratory of Software Engineering Wuhan China
    • Cheng Yang, Wuhan University State Key Laboratory of Software Engineering Wuhan China
    • Yuanxiang Li, Wuhan University State Key Laboratory of Software Engineering Wuhan China

Environment identification-based memory scheme for estimation of distribution algorithms in dynamic environments

Abstract  In estimation of distribution algorithms (EDAs), the joint probability distribution of high-performance solutions is presented
by a probability model. This means that the priority search areas of the solution space are characterize…

Abstract  In estimation of distribution algorithms (EDAs), the joint probability distribution of high-performance solutions is presented
by a probability model. This means that the priority search areas of the solution space are characterized by the probability
model. From this point of view, an environment identification-based memory management scheme (EI-MMS) is proposed to adapt
binary-coded EDAs to solve dynamic optimization problems (DOPs). Within this scheme, the probability models that characterize
the search space of the changing environment are stored and retrieved to adapt EDAs according to environmental changes. A
diversity loss correction scheme and a boundary correction scheme are combined to counteract the diversity loss during the
static evolutionary process of each environment. Experimental results show the validity of the EI-MMS and indicate that the
EI-MMS can be applied to any binary-coded EDAs. In comparison with three state-of-the-art algorithms, the univariate marginal
distribution algorithm (UMDA) using the EI-MMS performs better when solving three decomposable DOPs. In order to understand
the EI-MMS more deeply, the sensitivity analysis of parameters is also carried out in this paper.

  • Content Type Journal Article
  • Pages 1-16
  • DOI 10.1007/s00500-010-0547-5
  • Authors
    • Xingguang Peng, Northwestern Polytechnical University School of Electronics and Information Xi’an Shaanxi 710129 China
    • Xiaoguang Gao, Northwestern Polytechnical University School of Electronics and Information Xi’an Shaanxi 710129 China
    • Shengxiang Yang, University of Leicester Department of Computer Science University Road Leicester LE1 7RH UK

FOGA XI CFP

The 11th Foundations of Genetic Algorithms conference will be held on January 5-9, 2011 in Schwarzenberg, Austria. Submissions (10-12 pages) on the theoretical foundations of any type of evolutionary computation can be emailed to foga@fhv.at. The dea…

The 11th Foundations of Genetic Algorithms conference will be held on January 5-9, 2011 in Schwarzenberg, Austria. Submissions (10-12 pages) on the theoretical foundations of any type of evolutionary computation can be emailed to foga@fhv.at. The deadline for submissions is July 5, 2010. For more details see http://www.sigevo.org/foga-2011.

Evolutionary self-adaptation: a survey of operators and strategy parameters

Abstract  The success of evolutionary search depends on adequate parameter settings. Ill conditioned strategy parameters decrease the
success probabilities of genetic operators. Proper settings may change during the optimization process. The…

Abstract  

The success of evolutionary search depends on adequate parameter settings. Ill conditioned strategy parameters decrease the
success probabilities of genetic operators. Proper settings may change during the optimization process. The question arises
if adequate settings can be found automatically during the optimization process. Evolution strategies gave an answer to the
online parameter control problem decades ago: self-adaptation. Self-adaptation is the implicit search in the space of strategy
parameters. The self-adaptive control of mutation strengths in evolution strategies turned out to be exceptionally successful.
Nevertheless, for years self-adaptation has not achieved the attention it deserves. This paper is a survey of self-adaptive
parameter control in evolutionary computation. It classifies self-adaptation in the taxonomy of parameter setting techniques,
gives an overview of automatic online-controllable evolutionary operators and provides a coherent view on search techniques
in the space of strategy parameters. Beyer and Sendhoff’s covariance matrix self-adaptation evolution strategy is reviewed
as a successful example for self-adaptation and exemplarily tested for various concepts that are discussed.

  • Content Type Journal Article
  • DOI 10.1007/s12065-010-0035-y
  • Authors
    • Oliver Kramer, Technische Universität Dortmund Dortmund Germany

An ensemble-based evolutionary framework for coping with distributed intrusion detection

Abstract  A distributed data mining algorithm to improve the detection accuracy when classifying malicious or unauthorized network activity
is presented. The algorithm is based on genetic programming (GP) extended with the ensemble paradigm….

Abstract  

A distributed data mining algorithm to improve the detection accuracy when classifying malicious or unauthorized network activity
is presented. The algorithm is based on genetic programming (GP) extended with the ensemble paradigm. GP ensemble is particularly
suitable for distributed intrusion detection because it allows to build a network profile by combining different classifiers that together provide complementary information. The main novelty of the algorithm is
that data is distributed across multiple autonomous sites and the learner component acquires useful knowledge from this data
in a cooperative way. The network profile is then used to predict abnormal behavior. Experiments on the KDD Cup 1999 Data
show the capability of genetic programming in successfully dealing with the problem of intrusion detection on distributed
data.

  • Content Type Journal Article
  • Pages 131-146
  • DOI 10.1007/s10710-010-9101-6
  • Authors
    • Gianluigi Folino, Institute for High Performance Computing and Networking (ICAR) National Research Council (CNR) Via P. Bucci 41C 87036 Rende CS Italy
    • Clara Pizzuti, Institute for High Performance Computing and Networking (ICAR) National Research Council (CNR) Via P. Bucci 41C 87036 Rende CS Italy
    • Giandomenico Spezzano, Institute for High Performance Computing and Networking (ICAR) National Research Council (CNR) Via P. Bucci 41C 87036 Rende CS Italy