Live from Dublin

IlliGAL director David E. Goldberg is tweeting live from the IEEE Conference on Transforming Engineering Education at Dublin.  Right now there is a panel discussing whether transformation is needed and if so what needs to be transformed.  Follow the …

IlliGAL director David E. Goldberg is tweeting live from the IEEE Conference on Transforming Engineering Education at Dublin.  Right now there is a panel discussing whether transformation is needed and if so what needs to be transformed.  Follow the thread at www.twitter.com/deg511.

A neural network-based multi-agent classifier system with a Bayesian formalism for trust measurement

Abstract  In this paper, a neural network (NN)-based multi-agent classifier system (MACS) utilising the trust-negotiation-communication
(TNC) reasoning model is proposed. A novel trust measurement method, based on the combination of Bayesian…

Abstract  

In this paper, a neural network (NN)-based multi-agent classifier system (MACS) utilising the trust-negotiation-communication
(TNC) reasoning model is proposed. A novel trust measurement method, based on the combination of Bayesian belief functions,
is incorporated into the TNC model. The Fuzzy Min-Max (FMM) NN is used as learning agents in the MACS, and useful modifications
of FMM are proposed so that it can be adopted for trust measurement. Besides, an auctioning procedure, based on the sealed
bid method, is applied for the negotiation phase of the TNC model. Two benchmark data sets are used to evaluate the effectiveness
of the proposed MACS. The results obtained compare favourably with those from a number of machine learning methods. The applicability
of the proposed MACS to two industrial sensor data fusion and classification tasks is also demonstrated, with the implications
analysed and discussed.

  • Content Type Journal Article
  • DOI 10.1007/s00500-010-0592-0
  • Authors
    • Anas Quteishat, Al-Balqa’ Applied University Department of Computer Engineering, Faculty of Engineering Technology Al-Salt Jordan
    • Chee Peng Lim, University of Science Malaysia School of Electrical and Electronic Engineering Engineering Campus, 14300 Nibong Tebal Penang Malaysia
    • Junita Mohamad Saleh, University of Science Malaysia School of Electrical and Electronic Engineering Engineering Campus, 14300 Nibong Tebal Penang Malaysia
    • Jeffrey Tweedale, University of South Australia School of Electrical and Information Engineering Adelaide Australia
    • Lakhmi C. Jain, University of South Australia School of Electrical and Information Engineering Adelaide Australia

Dario Floreano and Claudio Mattiussi (eds): Bio-inspired artificial intelligence: theories, methods, and technologies

Dario Floreano and Claudio Mattiussi (eds): Bio-inspired artificial intelligence: theories, methods, and technologies
Content Type Journal ArticlePages 441-443DOI 10.1007/s10710-010-9104-3Authors
Ivan Garibay, University of Central Florida, Orlando,…

Dario Floreano and Claudio Mattiussi (eds): Bio-inspired artificial intelligence: theories, methods, and technologies

  • Content Type Journal Article
  • Pages 441-443
  • DOI 10.1007/s10710-010-9104-3
  • Authors
    • Ivan Garibay, University of Central Florida, Orlando, FL USA

Guest editorial: special issue on parallel and distributed evolutionary algorithms, part two

Guest editorial: special issue on parallel and distributed evolutionary algorithms, part two
Content Type Journal ArticlePages 129-130DOI 10.1007/s10710-010-9106-1Authors
Marco Tomassini, HEC, University of Lausanne Information Systems Department La…

Guest editorial: special issue on parallel and distributed evolutionary algorithms, part two

  • Content Type Journal Article
  • Pages 129-130
  • DOI 10.1007/s10710-010-9106-1
  • Authors
    • Marco Tomassini, HEC, University of Lausanne Information Systems Department Lausanne Switzerland
    • Leonardo Vanneschi, Systems and Communication (D.I.S.Co.), University of Milano-Bicocca Department of Informatics Milan Italy

State operators on GMV algebras

Abstract  Flaminio and Montagna recently introduced state

MV
algebras as

MV
algebras with an internal state in the form of a unary operation. Di Nola and Dvurečenskij further presented a stronger variation
of state

MV
algebras call…

Abstract  

Flaminio and Montagna recently introduced state

MV

algebras as

MV

algebras with an internal state in the form of a unary operation. Di Nola and Dvurečenskij further presented a stronger variation
of state

MV

algebras called state-morphism

MV

algebras. In the paper we present state

GMV

algebras and state-morphism

GMV

algebras which are non-commutative generalizations of the mentioned algebras.

  • Content Type Journal Article
  • DOI 10.1007/s00500-010-0568-0
  • Authors
    • Jiří Rachůnek, Palacký University Department of Algebra and Geometry, Faculty of Sciences Tomkova 40 779 00 Olomouc Czech Republic
    • Dana Šalounová, VŠB-Technical University Ostrava Department of Mathematical Methods in Economy, Faculty of Economics Sokolská 33 701 21 Ostrava Czech Republic

Automatic Reproduction of a Genius Algorithm: Strassen’s Algorithm Revisited by Genetic Search

In 1968, Volker Strassen, a young German mathematician, announced a clever algorithm to reduce the asymptotic complexity of nÿn matrix multiplication from the order of n 3 to n 2.81. It soon became one of the most famous scientific discoveries in the …

In 1968, Volker Strassen, a young German mathematician, announced a clever algorithm to reduce the asymptotic complexity of nÿn matrix multiplication from the order of n 3 to n 2.81. It soon became one of the most famous scientific discoveries in the 20th century and provoked numerous studies by other mathematicians to improve upon it. Although a number of improvements have been made, Strassen’s algorithm is still optimal in his original framework, the bilinear systems of 2 ÿ 2 matrix multiplication, and people are still curious how Strassen developed his algorithm. We examined it to see if we could automatically reproduce Strassen’s discovery using a search algorithm and find other algorithms of the same quality. In total, we found 608 algorithms that have the same quality as Strassen’s, including Strassen’s original algorithm. We partitioned the algorithms into nine different groups based on the way they are constructed. This paper was made possible by the combination of genetic search and linear-algebraic techniques. To the best of our knowledge, this is the first work that automatically reproduced Strassen’s algorithm, and furthermore, discovered new algorithms with equivalent asymptotic complexity using a search algorithm.

The CMA-ES on Riemannian Manifolds to Reconstruct Shapes in 3-D Voxel Images

The covariance matrix adaptation evolution strategy (CMA-ES) has been successfully used to minimize functionals on vector spaces. We generalize the concept of the CMA-ES to Riemannian manifolds and evaluate its performance in two experiments. First, we…

The covariance matrix adaptation evolution strategy (CMA-ES) has been successfully used to minimize functionals on vector spaces. We generalize the concept of the CMA-ES to Riemannian manifolds and evaluate its performance in two experiments. First, we minimize synthetic functionals on the 2-D sphere. Second, we consider the reconstruction of shapes in 3-D voxel data. A novel formulation of this problem leads to the minimization of edge and region-based segmentation functionals on the Riemannian manifold of parametric 3-D medial axis representation. We compare the results to gradient-based methods on manifolds and particle swarm optimization on tangent spaces and differential evolution.

Computational Evolutionary Embryogeny

Evolutionary and developmental processes are used to evolve the configurations of 3-D structures in silico to achieve desired performances. Natural systems utilize the combination of both evolution and development processes to produce remarkable perfor…

Evolutionary and developmental processes are used to evolve the configurations of 3-D structures in silico to achieve desired performances. Natural systems utilize the combination of both evolution and development processes to produce remarkable performance and diversity. However, this approach has not yet been applied extensively to the design of continuous 3-D load-supporting structures. Beginning with a single artificial cell containing information analogous to a DNA sequence, a structure is grown according to the rules encoded in the sequence. Each artificial cell in the structure contains the same sequence of growth and development rules, and each artificial cell is an element in a finite element mesh representing the structure of the mature individual. Rule sequences are evolved over many generations through selection and survival of individuals in a population. Modularity and symmetry are visible in nearly every natural and engineered structure. An understanding of the evolution and expression of symmetry and modularity is emerging from recent biological research. Initial evidence of these attributes is present in the phenotypes that are developed from the artificial evolution, although neither characteristic is imposed nor selected-for directly. The computational evolutionary development approach presented here shows promise for synthesizing novel configurations of high-performance systems. The approach may advance the system design to a new paradigm, where current design strategies have difficulty producing useful solutions.