Author: Community

GPEM 11(1) hardcopy — new color!

Subscribers should by now have received their hardcopy editions of GPEM 11(1), and noticed the attractive new blue color! Let me know what you think!

Subscribers should by now have received their hardcopy editions of GPEM 11(1), and noticed the attractive new blue color! Let me know what you think!

Deployment of parallel linear genetic programming using GPUs on PC and video game console platforms

Abstract  We present a general method for deploying parallel linear genetic programming (LGP) to the PC and Xbox 360 video game console
by using a publicly available common framework for the devices called XNA (for “XNA’s Not Acronymed

Abstract  

We present a general method for deploying parallel linear genetic programming (LGP) to the PC and Xbox 360 video game console
by using a publicly available common framework for the devices called XNA (for “XNA’s Not Acronymed”). By constructing the
LGP within this framework, we effectively produce an LGP “game” for PC and XBox 360 that displays results as they evolve.
We use the GPU of each device to parallelize fitness evaluation and the mutation operator of the LGP algorithm, thus providing
a general LGP implementation suitable for parallel computation on heterogeneous devices. While parallel GP implementations
on PCs are now common, both the implementation of GP on a video game console using GPU and the construction of a GP around
a framework for heterogeneous devices are novel contributions. The objective of this work is to describe how to implement
the parallel execution of LGP in order to use the underlying hardware (especially GPU) on the different platforms while still
maintaining loyalty to the general methodology of the LGP algorithm built for the common framework. We discuss the implementation
of texture-based data structures and the sequential and parallel algorithms built for their use on both CPU and GPU. Following
the description of the general algorithm, the particular tailoring of the implementations for each hardware platform is described.
Sequential (CPU) and parallel (GPU-based) algorithm performance is compared on both PC and video game platforms using the
metrics of GP operations per second, actual time elapsed, speedup of parallel over sequential implementation, and percentage
of execution time used by the GPU versus CPU.

  • Content Type Journal Article
  • Pages 147-184
  • DOI 10.1007/s10710-010-9102-5
  • Authors
    • Garnett Wilson, Memorial University of Newfoundland Department of Computer Science St. John’s NL A1B 3X5 Canada
    • Wolfgang Banzhaf, Memorial University of Newfoundland Department of Computer Science St. John’s NL A1B 3X5 Canada

Stabilization of nonlinear time-delay systems with input saturation via anti-windup fuzzy design

Abstract  This investigation considers stability analysis and control design for nonlinear time-delay systems subject to input saturation.
An anti-windup fuzzy control approach, based on fuzzy modeling of nonlinear systems, is developed to d…

Abstract  

This investigation considers stability analysis and control design for nonlinear time-delay systems subject to input saturation.
An anti-windup fuzzy control approach, based on fuzzy modeling of nonlinear systems, is developed to deal with the problems
of stabilization of the closed-loop system and enlargement of the domain of attraction. To facilitate the designing work,
the nonlinearity of saturation is first characterized by sector conditions, which provide a basis for analysis and synthesis
of the anti-windup fuzzy control scheme. Then, the Lyapunov–Krasovskii delay-independent and delay-dependent functional approaches
are applied to establish sufficient conditions that ensure convergence of all admissible initial states within the domain
of attraction. These conditions are formulated as a convex optimization problem with constraints provided by a set of linear
matrix inequalities. Finally, numeric examples are given to validate the proposed method.

  • Content Type Journal Article
  • Pages 1-12
  • DOI 10.1007/s00500-010-0555-5
  • Authors
    • Chen-Sheng Ting, National Formosa University Department of Electrical Engineering Yunlin 632 Taiwan, ROC
    • Chuan-Sheng Liu, National Formosa University Department of Aeronautical Engineering Yunlin 632 Taiwan, ROC

SIGEVOlution Volume 4 Issue 3

The new issue of SIGEVOlution is now available for you to download from:
http://www.sigevolution.org
The issue features:

Issues in Applying Computational Intelligence by Arthur Kordon
JavaXCSF by Patrick O. Stalph & Martin V. Butz
Dissertation Corner
New issues of journals
Calls & calendar

The newsletter is intended to be viewed electronically.
Pier Luca Lanzi (EIC)
Related Posts

Portland, Oregon

The new issue of SIGEVOlution is now available for you to download from:
http://www.sigevolution.org

The issue features:

  • Issues in Applying Computational Intelligence by Arthur Kordon
  • JavaXCSF by Patrick O. Stalph & Martin V. Butz
  • Dissertation Corner
  • New issues of journals
  • Calls & calendar

The newsletter is intended to be viewed electronically.

Pier Luca Lanzi (EIC)

Related Posts

    Electrostatic field framework for supervised and semi-supervised learning from incomplete data

    Abstract  In this paper a classification framework for incomplete data, based on electrostatic field model is proposed. An original
    approach to exploiting incomplete training data with missing features, involving extensive use of electrostat…

    Abstract  

    In this paper a classification framework for incomplete data, based on electrostatic field model is proposed. An original
    approach to exploiting incomplete training data with missing features, involving extensive use of electrostatic charge analogy,
    has been used. The framework supports a hybrid supervised and unsupervised training scenario, enabling learning simultaneously
    from both labelled and unlabelled data using the same set of rules and adaptation mechanisms. Classification of incomplete
    patterns has been facilitated by introducing a local dimensionality reduction technique, which aims at exploiting all available
    information using the data ‘as is’, rather than trying to estimate the missing values. The performance of all proposed methods
    has been extensively tested in a wide range of missing data scenarios, using a number of standard benchmark datasets in order
    to make the results comparable with those available in current and future literature. Several modifications to the original
    Electrostatic Field Classifier aiming at improving speed and robustness in higher dimensional spaces have also been introduced
    and discussed.

    • Content Type Journal Article
    • Pages 921-945
    • DOI 10.1007/s11047-010-9182-4
    • Authors
      • Marcin Budka, Computational Intelligence Research Group, School of Design, Engineering & Computing, Bournemouth University, Poole House, Talbot Campus, Fern Barrow, Poole, BH12 5BB UK
      • Bogdan Gabrys, Computational Intelligence Research Group, School of Design, Engineering & Computing, Bournemouth University, Poole House, Talbot Campus, Fern Barrow, Poole, BH12 5BB UK

    A hierarchical multiclass support vector machine incorporated with holistic triple learning units

    Abstract  This paper proposes a new hierarchical learning structure, namely the holistic triple learning (HTL), for extending the binary
    support vector machine (SVM) to multi-classification problems. For an N-class problem, a HTL constructs …

    Abstract  

    This paper proposes a new hierarchical learning structure, namely the holistic triple learning (HTL), for extending the binary
    support vector machine (SVM) to multi-classification problems. For an N-class problem, a HTL constructs a decision tree up to a depth of

    éN/3ù+1

    . A leaf node of the decision tree is allowed to be placed with a holistic triple learning unit whose generalisation abilities
    are assessed and approved. Meanwhile, the remaining nodes in the decision tree each accommodate a standard binary SVM classifier.
    The holistic triple classifier is a regression model trained on three classes, whose training algorithm is originated from
    a recently proposed implementation technique, namely the least-squares support vector machine (LS-SVM). A major novelty with
    the holistic triple classifier is the reduced number of support vectors in the solution. For the resultant HTL-SVM, an upper
    bound of the generalisation error can be obtained. The time complexity of training the HTL-SVM is analysed, and is shown to
    be comparable to that of training the one-versus-one (1-vs.-1) SVM, particularly on small-scale datasets. Empirical studies
    show that the proposed HTL-SVM achieves competitive classification accuracy with a reduced number of support vectors compared
    to the popular 1-vs-1 alternative.

    • Content Type Journal Article
    • Pages 1-11
    • DOI 10.1007/s00500-010-0551-9
    • Authors
      • Xiao-Lei Xia, Queen’s University of Belfast Intelligent Systems and Control, School of Electronics, Electrical Engineering and Computer Science Belfast BT9 5AH UK
      • Kang Li, Queen’s University of Belfast Intelligent Systems and Control, School of Electronics, Electrical Engineering and Computer Science Belfast BT9 5AH UK
      • George W. Irwin, Queen’s University of Belfast Intelligent Systems and Control, School of Electronics, Electrical Engineering and Computer Science Belfast BT9 5AH UK

    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