Tag: Soft Computing – A Fusion of Foundations, Methodologies and Applications

On Buckley’s approach to fuzzy estimation

Abstract  In several works, Buckley (Soft Comput 9:512–518, 2005a; Soft Comput 9:769–775 2005b; Fuzzy statistics, Springer, Heidelberg, 2005c) have introduced and developed an approach to the estimation of unknown parameters in statistica…

Abstract  

In several works, Buckley (Soft Comput 9:512–518, 2005a; Soft Comput 9:769–775 2005b; Fuzzy statistics, Springer, Heidelberg, 2005c) have introduced and developed an approach to the estimation of unknown parameters in statistical models. In this paper,
we introduce an improved method for the estimation of parameters for cases in which the Buckley’s approach presents some drawbacks,
as for example when the underlying statistic has a non-symmetric distribution.

  • Content Type Journal Article
  • DOI 10.1007/s00500-010-0619-6
  • Authors
    • Ali Falsafain, Shahid Bahonar University of Kerman Department of Statistics, Faculty of Mathematics and Computer Sciences Kerman Iran
    • S. Mahmoud Taheri, Isfahan University of Technology Department of Mathematical Sciences Isfahan 84156-83111 Iran

Structural learning of Bayesian networks using local algorithms based on the space of orderings

Abstract  Structural learning of Bayesian networks (BNs) is an NP-hard problem which is generally addressed by means of heuristic search
algorithms. Despite the fact that earlier proposals for dealing with this task were based on searching t…

Abstract  

Structural learning of Bayesian networks (BNs) is an NP-hard problem which is generally addressed by means of heuristic search
algorithms. Despite the fact that earlier proposals for dealing with this task were based on searching the space of Directed
Acyclic Graphs (DAGs), there are some alternative approaches. One of these approaches for structural learning consists of
searching the space of orderings, as given a certain topological order among the problem variables, it is relatively easy
to build (and evaluate) a BN compatible with it. In practice, the latter methods make it possible to obtain good results,
but they are still costly in terms of computation. In this article, we prove the correctness of the method used to evaluate
each ordering, and we propose some efficient learning algorithms based on it. Our first proposal is based on the Hill-Climbing
algorithm, and uses an improved neighbourhood definition. The second algorithm is an extension of the first one, and is based
on the well-known Variable Neighbourhood Search metaheuristic. Finally, iterative versions of both algorithms are also proposed.
The algorithms have been tested over a set of different domains, and have been compared with other methods such as Hill-Climbing
in the space of DAGs or Greedy Equivalent Search, in order to study their behaviour in practice.

  • Content Type Journal Article
  • DOI 10.1007/s00500-010-0623-x
  • Authors
    • Juan I. Alonso-Barba, University of Castilla-La Mancha Laboratory of Intelligent Systems and Data Mining, Albacete Research Institute of Informatics 02071 Albacete Spain
    • Luis delaOssa, University of Castilla-La Mancha Laboratory of Intelligent Systems and Data Mining, Albacete Research Institute of Informatics 02071 Albacete Spain
    • Jose M. Puerta, University of Castilla-La Mancha Laboratory of Intelligent Systems and Data Mining, Albacete Research Institute of Informatics 02071 Albacete Spain

Implementation of cellular genetic algorithms with two neighborhood structures for single-objective and multi-objective optimization

Abstract  In cellular algorithms, a single neighborhood structure for local selection is usually assumed to specify a set of neighbors
for each cell. There exist, however, a number of examples with two neighborhood structures in nature. One …

Abstract  

In cellular algorithms, a single neighborhood structure for local selection is usually assumed to specify a set of neighbors
for each cell. There exist, however, a number of examples with two neighborhood structures in nature. One is for local selection
for mating, and the other is for local competition such as the fight for water and sunlight among neighboring plants. The
aim of this paper is to show several implementations of cellular algorithms with two neighborhood structures for single-objective
and multi-objective optimization problems. Since local selection has already been utilized in cellular algorithms in the literature,
the main issue of this paper is how to implement the concept of local competition. We show three ideas about its utilization:
Local elitism, local ranking, and local replacement. Local elitism and local ranking are used for single-objective optimization
to increase the diversity of solutions. On the other hand, local replacement is used for multi-objective optimization to improve
the convergence of solutions to the Pareto frontier. The main characteristic feature of our approach is that the two neighborhood
structures can be specified independently of each other. Thus, we can separately examine the effect of each neighborhood structure
on the behavior of cellular algorithms.

  • Content Type Journal Article
  • DOI 10.1007/s00500-010-0617-8
  • Authors
    • Hisao Ishibuchi, Osaka Prefecture University Graduate School of Engineering Osaka Japan
    • Yuji Sakane, Osaka Prefecture University Graduate School of Engineering Osaka Japan
    • Noritaka Tsukamoto, Osaka Prefecture University Graduate School of Engineering Osaka Japan
    • Yusuke Nojima, Osaka Prefecture University Graduate School of Engineering Osaka Japan

The use of coevolution and the artificial immune system for ensemble learning

Abstract  This paper presents two new approaches for constructing an ensemble of neural networks (NN) using coevolution and the artificial
immune system (AIS). These approaches are extensions of the CLONal Selection Algorithm for building EN…

Abstract  

This paper presents two new approaches for constructing an ensemble of neural networks (NN) using coevolution and the artificial
immune system (AIS). These approaches are extensions of the CLONal Selection Algorithm for building ENSembles (CLONENS) algorithm.
An explicit diversity promotion technique was added to CLONENS and a novel coevolutionary approach to build neural ensembles
is introduced, whereby two populations representing the gates and the individual NN are coevolved. The former population is
responsible for defining the ensemble size and selecting the members of the ensemble. This population is evolved using the
differential evolution algorithm. The latter population supplies the best individuals for building the ensemble, which is
evolved by AIS. Results show that it is possible to automatically define the ensemble size being also possible to find smaller
ensembles with good generalization performance on the tested benchmark regression problems. More interestingly, the use of
the diversity measure during the evolutionary process did not necessarily improve generalization. In this case, diverse ensembles
may be found using only implicit diversity promotion techniques.

  • Content Type Journal Article
  • DOI 10.1007/s00500-010-0613-z
  • Authors
    • Bruno H. G. Barbosa, Federal University of Lavras Department of Engineering Lavras MG Brazil
    • Lam T. Bui, University of New South Wales School of Information Technology and Electrical Engineering, Australian Defence Force Academy Canberra ACT Australia
    • Hussein A. Abbass, University of New South Wales School of Information Technology and Electrical Engineering, Australian Defence Force Academy Canberra ACT Australia
    • Luis A. Aguirre, Federal University of Minas Gerais Department of Electronic Engineering Belo Horizonte MG Brazil
    • Antônio P. Braga, Federal University of Minas Gerais Department of Electronic Engineering Belo Horizonte MG Brazil

Crossover can be constructive when computing unique input–output sequences

Abstract  Unique input–output (UIO) sequences have important applications in conformance testing of finite state machines (FSMs). Previous
experimental and theoretical research has shown that evolutionary algorithms (EAs) can compute UIOs …

Abstract  

Unique input–output (UIO) sequences have important applications in conformance testing of finite state machines (FSMs). Previous
experimental and theoretical research has shown that evolutionary algorithms (EAs) can compute UIOs efficiently on many FSM
instance classes, but fail on others. However, it has been unclear how and to what degree EA parameter settings influence
the runtime on the UIO problem. This paper investigates the choice of acceptance criterion in the (1 + 1) EA and the use of
crossover in the

(m+1)

Steady State Genetic Algorithm. It is rigorously proved that changing these parameters can reduce the runtime from exponential
to polynomial for some instance classes of the UIO problem.

  • Content Type Journal Article
  • DOI 10.1007/s00500-010-0610-2
  • Authors
    • Per Kristian Lehre, The University of Birmingham The Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA), School of Computer Science Edgbaston Birmingham B15 2TT UK
    • Xin Yao, The University of Birmingham The Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA), School of Computer Science Edgbaston Birmingham B15 2TT UK

Particle swarm optimisation based AdaBoost for object detection

Abstract  This paper proposes a new approach to using particle swarm optimisation (PSO) within an AdaBoost framework for object detection.
Instead of using exhaustive search for finding good features to be used for constructing weak classifi…

Abstract  

This paper proposes a new approach to using particle swarm optimisation (PSO) within an AdaBoost framework for object detection.
Instead of using exhaustive search for finding good features to be used for constructing weak classifiers in AdaBoost, we
propose two methods based on PSO. The first uses PSO to evolve and select good features only, and the weak classifiers use
a simple decision stump. The second uses PSO for both selecting good features and evolving weak classifiers in parallel. These
two methods are examined and compared on two challenging object detection tasks in images: detection of individual pasta pieces
and detection of a face. The experimental results suggest that both approaches can successfully detect object positions and
that using PSO for selecting good individual features and evolving associated weak classifiers in AdaBoost is more effective
than for selecting features only. We also show that PSO can evolve and select meaningful features in the face detection task.

  • Content Type Journal Article
  • DOI 10.1007/s00500-010-0615-x
  • Authors
    • Ammar Mohemmed, Victoria University of Wellington School of Engineering and Computer Science PO Box 600 Wellington New Zealand
    • Mark Johnston, Victoria University of Wellington School of Mathematics, Statistics and Operations Research PO Box 600 Wellington New Zealand
    • Mengjie Zhang, Victoria University of Wellington School of Engineering and Computer Science PO Box 600 Wellington New Zealand

Guest Editorial: special issue on evolutionary optimisation and learning

Guest Editorial: special issue on evolutionary optimisation and learning
Content Type Journal ArticleDOI 10.1007/s00500-010-0609-8Authors
Mengjie Zhang, Victoria University of Wellington School of Engineering and Computer Science PO Box 600 Wellingt…

Guest Editorial: special issue on evolutionary optimisation and learning

  • Content Type Journal Article
  • DOI 10.1007/s00500-010-0609-8
  • Authors
    • Mengjie Zhang, Victoria University of Wellington School of Engineering and Computer Science PO Box 600 Wellington New Zealand
    • Michael Kirley, The University of Melbourne Department of Computer Science and Software Engineering, Melbourne School of Engineering Melbourne VIC 3001 Australia
    • Xiaodong Li, RMIT University School of Computer Science and Information Technology Melbourne VIC 3001 Australia

Robust path tracking control of mobile robot via dynamic petri recurrent fuzzy neural network

Abstract  This study focuses on the design of robust path tracking control for a mobile robot via a dynamic Petri recurrent fuzzy neural
network (DPRFNN). In the DPRFNN, the concept of a Petri net (PN) and the recurrent frame of internal fee…

Abstract  

This study focuses on the design of robust path tracking control for a mobile robot via a dynamic Petri recurrent fuzzy neural
network (DPRFNN). In the DPRFNN, the concept of a Petri net (PN) and the recurrent frame of internal feedback loops are incorporated
into a traditional fuzzy neural network (FNN) to alleviate the computation burden of parameter learning and to enhance the
dynamic mapping of network ability. This five-layer DPRFNN is utilized for the major role in the proposed control scheme,
and the corresponding adaptation laws of network parameters are established in the sense of projection algorithm and Lyapunov
stability theorem to ensure the network convergence as well as stable control performance without the requirement of detailed
system information and the compensation of auxiliary controllers. In addition, the effectiveness of the proposed robust DPRFNN
control scheme is verified by experimental results of a differential-driving mobile robot under different moving paths and
the occurrence of uncertainties, and its superiority is indicated in comparison with a stabilizing control system.

  • Content Type Journal Article
  • DOI 10.1007/s00500-010-0607-x
  • Authors
    • Rong-Jong Wai, Yuan Ze University Department of Electrical Engineering and Fuel Cell Center Chung Li Taiwan, ROC
    • Chia-Ming Liu, Yuan Ze University Department of Electrical Engineering and Fuel Cell Center Chung Li Taiwan, ROC
    • You-Wei Lin, Yuan Ze University Department of Electrical Engineering and Fuel Cell Center Chung Li Taiwan, ROC

Comment on: robust stability of stochastic genetic regulatory networks with discrete and distributed delays

Abstract  This comment points out to some mistakes in the main theorem of Wang (Soft Comput, 13(12):1199–1208, 2009) concerning stochastic
robust stability of genetic regulatory networks (GRNs). The inequalities in the theorem are not LMIs…

Abstract  

This comment points out to some mistakes in the main theorem of Wang (Soft Comput, 13(12):1199–1208, 2009) concerning stochastic
robust stability of genetic regulatory networks (GRNs). The inequalities in the theorem are not LMIs. Moreover, the proof
of theorem is based on a sector condition that was expressed incorrectly and thus invalidates the proof. The correct LMIs
are provided in this comment.

  • Content Type Journal Article
  • DOI 10.1007/s00500-010-0608-9
  • Authors
    • Alireza Salimpour, Tarbiat Modares University Intelligent Control Systems Laboratory, School of Electrical and Computer Engineering P.O. Box 14115-194 Tehran Iran
    • Vahid Johari Majd, Tarbiat Modares University Intelligent Control Systems Laboratory, School of Electrical and Computer Engineering P.O. Box 14115-194 Tehran Iran
    • Mahdi Sojoodi, Tarbiat Modares University Intelligent Control Systems Laboratory, School of Electrical and Computer Engineering P.O. Box 14115-194 Tehran Iran

A definition for I-fuzzy partitions

Abstract  In this paper, we define I-fuzzy partitions (or intuitionistic fuzzy partitions as called by Atanassov or interval-valued
fuzzy partitions). As our ultimate goal is to compare the results of standard fuzzy clustering algorithms (e….

Abstract  

In this paper, we define I-fuzzy partitions (or intuitionistic fuzzy partitions as called by Atanassov or interval-valued
fuzzy partitions). As our ultimate goal is to compare the results of standard fuzzy clustering algorithms (e.g. fuzzy c-means), we define a method to construct them from a set of fuzzy clusters obtained from several executions of fuzzy c-means. From a practical point of view, the approach presented here tries to solve the difficulty of comparing the results
of fuzzy clustering methods and, in particular, the difficulty of finding the global optimal.

  • Content Type Journal Article
  • DOI 10.1007/s00500-010-0605-z
  • Authors
    • Vicenç Torra, CSIC, Spanish Council for Scientific Research IIIA, Institut d’Investigació en Intel-ligència Artificial Campus de Bellaterra 08193 Bellaterra Catalonia Spain
    • Sadaaki Miyamoto, University of Tsukuba Department of Risk Engineering, School of Systems and Information Engineering Tsukuba Ibaraki 305-8573 Japan