The perils and pleasures of interdisciplinarity

IlliGAL director David E. Goldberg just gave a talk on “The Perils and Pleasures of Interdisciplinarity” at a Workshop on the Challenges in Top-Down, Bottom-up and Computational Approaches in Synthetic Biology.  The talk is available in the viewer below:[slideshare id=3465878&doc=deg-practical-philosophical-reflections-3-2010-100318072813-phpapp02]Related talks are available here.
Related Posts

IlliGAL director David E. Goldberg just gave a talk on “The Perils and Pleasures of Interdisciplinarity” at a Workshop on the Challenges in Top-Down, Bottom-up and Computational Approaches in Synthetic Biology.  The talk is available in the viewer below:[slideshare id=3465878&doc=deg-practical-philosophical-reflections-3-2010-100318072813-phpapp02]Related talks are available here.

Related Posts

    Soaring the Clouds with Meandre

    You may find the slide deck and the abstract for the presentation we delivered today at the “Data-Intensive Research: how should we improve our ability to use data” workshop in Edinburgh. Abstract This talk will focus a highly scalable data intensive infrastructure being developed at the National Center for Supercomputing Application (NCSA) at the University […]

    Related posts:

    1. Meandre: Semantic-Driven Data-Intensive Flows in the Clouds
    2. Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study using Meandre
    3. [BDCSG2008] Clouds and ManyCores: The Revolution (Dan Reed)

    You may find the slide deck and the abstract for the presentation we delivered today at the “Data-Intensive Research: how should we improve our ability to use data” workshop in Edinburgh.

    Abstract

    This talk will focus a highly scalable data intensive infrastructure being developed at the National Center for Supercomputing Application (NCSA) at the University of Illinois and will introduce current research efforts to tackle the challenges presented by big-data. Research efforts include exploring potential ways of integration between cloud computing concepts—such as Hadoop or Meandre—and traditional HPC technologies and assets. These architecture models contrast significantly, but can be leveraged by building cloud conduits that connect these resources to provide even greater flexibility and scalability on demand. Orchestrating the physical computational environment requires innovative and sophisticated software infrastructure that can transparently take advantage of the functional features and to negotiate the constraints imposed by this diversity of computational resources. Research conducted during the development of the Meandre infrastructure has lead to the production of an agile conductor able to leverage the particular advantages in the physical diversity. It can also be implemented as services and/or in the context of another application benefitting from it reusability, flexibility, and high-scalability. Some example applications and an introduction to the data intensive infrastructure architecture will be presented to provide an overview of the diverse scope of Meandre usages. Finally, a case will be presented showing how software developers and system designers can easily transition to these new paradigms to address the primary data-deluge challenges and to soar to new heights with extreme application scalability using cloud computing concepts.

    Related posts:

    1. Meandre: Semantic-Driven Data-Intensive Flows in the Clouds
    2. Data-Intensive Computing for Competent Genetic Algorithms: A Pilot Study using Meandre
    3. [BDCSG2008] Clouds and ManyCores: The Revolution (Dan Reed)

    New MEDAL reports available online

    We are pleased to announce the following MEDAL technical reports:
    MEDAL Report No. 2010005
    Loopy Substructural Local Search for the Bayesian Optimization Algorithm
    Claudio F. Lima, Martin Pelikan, Fernando G. Lobo, and David E. Goldberg (2010)
    [Abstract] [Download PDF]
    MEDAL Report No. 2010004
    Model Accuracy in the Bayesian Optimization Algorithm
    Claudio F. Lima, Fernando G. Lobo, Martin Pelikan, and David E. […]

    We are pleased to announce the following MEDAL technical reports:

    MEDAL Report No. 2010005
    Loopy Substructural Local Search for the Bayesian Optimization Algorithm
    Claudio F. Lima, Martin Pelikan, Fernando G. Lobo, and David E. Goldberg (2010)
    [Abstract] [Download PDF]

    MEDAL Report No. 2010004
    Model Accuracy in the Bayesian Optimization Algorithm
    Claudio F. Lima, Fernando G. Lobo, Martin Pelikan, and David E. Goldberg (2010)
    [Abstract] [Download PDF]

    MEDAL Report No. 2010003
    Network crossover performance on NK landscapes and deceptive problems
    Mark W Hauschild and Martin Pelikan (2010)
    [Abstract] [Download PDF]

    MEDAL Report No. 2010002
    Spurious Dependencies and EDA Scalability
    Elizabeth Radetic and Martin Pelikan (2010)
    [Abstract] [Download PDF]

    MEDAL Report No. 2010001
    NK Landscapes, Problem Difficulty, and Hybrid Evolutionary Algorithms
    Martin Pelikan (2010)
    [Abstract] [Download PDF]

    Variable population size and evolution acceleration: a case study with a parallel evolutionary algorithm

    Abstract  With current developments of parallel and distributed computing, evolutionary algorithms have benefited considerably from
    parallelization techniques. Besides improved computation efficiency, parallelization may bring about innovati…

    Abstract  

    With current developments of parallel and distributed computing, evolutionary algorithms have benefited considerably from
    parallelization techniques. Besides improved computation efficiency, parallelization may bring about innovation to many aspects
    of evolutionary algorithms. In this article, we focus on the effect of variable population size on accelerating evolution
    in the context of a parallel evolutionary algorithm. In nature it is observed that dramatic variations of population size
    have considerable impact on evolution. Interestingly, the property of variable population size here arises implicitly and
    naturally from the algorithm rather than through intentional design. To investigate the effect of variable population size
    in such a parallel algorithm, evolution dynamics, including fitness progression and population diversity variation, are analyzed.
    Further, this parallel algorithm is compared to a conventional fixed-population-size genetic algorithm. We observe that the
    dramatic changes in population size allow evolution to accelerate.

    • Content Type Journal Article
    • Pages 205-225
    • DOI 10.1007/s10710-010-9105-2
    • Authors
      • Ting Hu, Memorial University Department of Computer Science St. John’s NL Canada
      • Simon Harding, Memorial University Department of Computer Science St. John’s NL Canada
      • Wolfgang Banzhaf, Memorial University Department of Computer Science St. John’s NL Canada

    On intuitionistic fuzzy topologies based on intuitionistic fuzzy reflexive and transitive relations

    Abstract  Topologies and rough set theory are widely used in the research field of machine learning and cybernetics. An intuitionistic
    fuzzy rough set, which is the result of approximation of an intuitionistic fuzzy set with respect to an in…

    Abstract  

    Topologies and rough set theory are widely used in the research field of machine learning and cybernetics. An intuitionistic
    fuzzy rough set, which is the result of approximation of an intuitionistic fuzzy set with respect to an intuitionistic fuzzy
    approximation space, is an extension of fuzzy rough sets. For further studying the theories and applications of intuitionistic
    fuzzy rough sets, in this paper, we investigate the topological structures of intuitionistic fuzzy rough sets. We show that
    an intuitionistic fuzzy rough approximation space can induce an intuitionistic fuzzy topological space in the sense of Lowen
    if and only if the intuitionistic fuzzy relation in the approximation space is reflexive and transitive. We also examine the
    sufficient and necessary conditions that an intuitionistic fuzzy topological space can be associated with an intuitionistic
    fuzzy reflexive and transitive relation such that the induced lower and upper intuitionistic fuzzy rough approximation operators
    are, respectively, the intuitionistic fuzzy interior and closure operators of the given topology.

    • Content Type Journal Article
    • Pages 1-12
    • DOI 10.1007/s00500-010-0576-0
    • Authors
      • Wei-Zhi Wu, Zhejiang Ocean University School of Mathematics, Physics and Information Science Zhoushan 316004 Zhejiang People’s Republic of China
      • Lei Zhou, Chengdu University of Information Technology College of Mathematics Chengdu 610225 Sichuan People’s Republic of China

    Heterogeneous computing scheduling with evolutionary algorithms

    Abstract  This work presents sequential and parallel evolutionary algorithms (EAs) applied to the scheduling problem in heterogeneous
    computing environments, a NP-hard problem with capital relevance in distributed computing. These methods ha…

    Abstract  

    This work presents sequential and parallel evolutionary algorithms (EAs) applied to the scheduling problem in heterogeneous
    computing environments, a NP-hard problem with capital relevance in distributed computing. These methods have been specifically
    designed to provide accurate and efficient solutions by using simple operators that allow them to be later extended for solving
    realistic problem instances arising in distributed heterogeneous computing (HC) and grid systems. The EAs were codified over
    MALLBA, a general-purpose library for combinatorial optimization. Efficient numerical results are reported in the experimental
    analysis performed on well-known problem instances. The comparative study of scheduling methods shows that the parallel versions
    of the implemented evolutionary algorithms are able to achieve high problem solving efficacy, outperforming traditional scheduling
    heuristics and also improving over previous results already reported in the related literature.

    • Content Type Journal Article
    • Pages 1-17
    • DOI 10.1007/s00500-010-0594-y
    • Authors
      • Sergio Nesmachnow, Universidad de la República Montevideo Uruguay
      • Héctor Cancela, Universidad de la República Montevideo Uruguay
      • Enrique Alba, Universidad de Málaga Málaga Spain

    Semi-supervised model applied to the prediction of the response to preoperative chemotherapy for breast cancer

    Abstract  Breast cancer is the second most frequent one, and the first one affecting the women. The standard treatment has three main
    stages: a preoperative chemotherapy followed by a surgery operation, then an post-operatory chemotherapy. B…

    Abstract  

    Breast cancer is the second most frequent one, and the first one affecting the women. The standard treatment has three main
    stages: a preoperative chemotherapy followed by a surgery operation, then an post-operatory chemotherapy. Because the response
    to the preoperative chemotherapy is correlated to a good prognosis, and because the clinical and biological information do
    not yield to efficient predictions of the response, a lot of research effort is being devoted to the design of predictors
    relying on the measurement of genes’ expression levels. In the present paper, we report our works for designing genomic predictors
    of the response to the preoperative chemotherapy, making use of a semi-supervised machine learning approach. The method is
    based on margin geometric information of patterns of low density areas, computed on a labeled dataset and on an unlabeled
    one.

    • Content Type Journal Article
    • Pages 1-8
    • DOI 10.1007/s00500-010-0589-8
    • Authors
      • Frederico Coelho, PPGEE, CPDEE Universidade Federal de Minas Gerais Belo Horizonte Brazil
      • Antônio de Pádua Braga, PPGEE, CPDEE Universidade Federal de Minas Gerais Belo Horizonte Brazil
      • René Natowicz, Université Paris-Est ESIEE-Paris, Département d’ínformatiquex Paris France
      • Roman Rouzier, Hôpital Tenon Départment of Gynecology Paris France

    The inheritance of BDE-property in sharply dominating lattice effect algebras and (o)-continuous states

    Abstract  We study remarkable sub-lattice effect algebras of Archimedean atomic lattice effect algebras E, namely their blocks M, centers C(E), compatibility centers B(E) and sets of all sharp elements S(E) of E. We show that in every such ef…

    Abstract  

    We study remarkable sub-lattice effect algebras of Archimedean atomic lattice effect algebras E, namely their blocks M, centers C(E), compatibility centers B(E) and sets of all sharp elements S(E) of E. We show that in every such effect algebra E, every atomic block M and the set S(E) are bifull sub-lattice effect algebras of E. Consequently, if E is moreover sharply dominating then every atomic block M is again sharply dominating and the basic decompositions of elements (BDE of x) in E and in M coincide. Thus in the compatibility center B(E) of E, nonzero elements are dominated by central elements and their basic decompositions coincide with those in all atomic blocks
    and in E. Some further details which may be helpful under answers about the existence and properties of states are shown. Namely,
    we prove the existence of an (o)-continuous state on every sharply dominating Archimedean atomic lattice effect algebra E with


    B(E)\not = C(E).

    Moreover, for compactly generated Archimedean lattice effect algebras the equivalence of (o)-continuity of states with their complete additivity is proved. Further, we prove “State smearing theorem” for these lattice
    effect algebras.

    • Content Type Journal Article
    • Pages 1-13
    • DOI 10.1007/s00500-010-0561-7
    • Authors
      • Jan Paseka, Masaryk University Department of Mathematics and Statistics, Faculty of Science Kotlářská 2 611 37 Brno Czech Republic
      • Zdenka Riečanová, Slovak University of Technology Department of Mathematics, Faculty of Electrical Engineering and Information Technology Ilkovičova 3 812 19 Bratislava Slovak Republic

    Optimization of interval type-2 fuzzy logic controllers using evolutionary algorithms

    Abstract  A method for designing optimal interval type-2 fuzzy logic controllers using evolutionary algorithms is presented in this
    paper. Interval type-2 fuzzy controllers can outperform conventional type-1 fuzzy controllers when the proble…

    Abstract  

    A method for designing optimal interval type-2 fuzzy logic controllers using evolutionary algorithms is presented in this
    paper. Interval type-2 fuzzy controllers can outperform conventional type-1 fuzzy controllers when the problem has a high
    degree of uncertainty. However, designing interval type-2 fuzzy controllers is more difficult because there are more parameters
    involved. In this paper, interval type-2 fuzzy systems are approximated with the average of two type-1 fuzzy systems, which
    has been shown to give good results in control if the type-1 fuzzy systems can be obtained appropriately. An evolutionary
    algorithm is applied to find the optimal interval type-2 fuzzy system as mentioned above. The human evolutionary model is
    applied for optimizing the interval type-2 fuzzy controller for a particular non-linear plant and results are compared against
    an optimal type-1 fuzzy controller. A comparative study of simulation results of the type-2 and type-1 fuzzy controllers,
    under different noise levels, is also presented. Simulation results show that interval type-2 fuzzy controllers obtained with
    the evolutionary algorithm outperform type-1 fuzzy controllers.

    • Content Type Journal Article
    • Pages 1-16
    • DOI 10.1007/s00500-010-0588-9
    • Authors
      • O. Castillo, Tijuana, Institute of Technology Tijuana BC Mexico
      • P. Melin, Tijuana, Institute of Technology Tijuana BC Mexico
      • A. Alanis, Tijuana, Institute of Technology Tijuana BC Mexico
      • O. Montiel, Center for Research in Digital Systems, IPN Tijuana BC Mexico
      • R. Sepulveda, Center for Research in Digital Systems, IPN Tijuana BC Mexico

    A hybrid neural network cybernetic system for quantifying cross-market dynamics and business forecasting

    Abstract  The internal structure of a complex system can manifest itself with correlations among its components. In global business,
    the interactions between different markets cause collective lead–lag behavior having special statistical p…

    Abstract  

    The internal structure of a complex system can manifest itself with correlations among its components. In global business,
    the interactions between different markets cause collective lead–lag behavior having special statistical properties which
    reflect the underlying dynamics. In this work, a cybernetic system of combining the vector autoregression (VAR) and genetic
    algorithm (GA) with neural network (NN) is proposed to take advantage of the lead–lag dynamics, to make the NN forecasting
    process more transparent and to improve the NN’s prediction capability. Two business case studies are carried out to demonstrate
    the advantages of our proposed system. The first one is the tourism demand forecasting for the Hong Kong market. Another business
    case study is the modeling and forecasting of Asian Pacific stock markets. The multivariable time series data is investigated
    with the VAR analysis, and then the NN is fed with the relevant variables determined by the VAR analysis for forecasting.
    Lastly, GA is used to cope with the time-dependent nature of the co-relationships among the variables. Experimental results
    show that our system is more robust and makes more accurate prediction than the benchmark NN. The contribution of this paper
    lies in the novel application of the forecasting modules and the high degree of transparency of the forecasting process.

    • Content Type Journal Article
    • Pages 1-13
    • DOI 10.1007/s00500-010-0580-4
    • Authors
      • S. I. Ao, International Association of Engineers Hong Kong China