Category: Uncategorized

The numerical solution of linear fuzzy Fredholm integral equations of the second kind by using finite and divided differences methods

Abstract  In recent years, many numerical methods have been proposed for solving fuzzy linear integral equations. In this paper, we
use the divided differences and finite differences methods for solving a parametric of the fuzzy Fredholm int…

Abstract  

In recent years, many numerical methods have been proposed for solving fuzzy linear integral equations. In this paper, we
use the divided differences and finite differences methods for solving a parametric of the fuzzy Fredholm integral equations
of the second kind with arbitrary kernel and present some examples to illustrate this method.

  • Content Type Journal Article
  • DOI 10.1007/s00500-010-0606-y
  • Authors
    • N. Parandin, Islamic Azad University Department of Mathematics, Kermanshah Branch Kermanshah Iran
    • M. A. Fariborzi Araghi, Islamic Azad University Department of Mathematics, Central Tehran Branch P.O. Box 13185.768 Tehran Iran

A genetic programming method for protein motif discovery and protein classification

Abstract  Proteins can be grouped into families according to some features such as hydrophobicity, composition or structure, aiming
to establish common biological functions. This paper presents MAHATMA—memetic algorithm-based highly adapte…

Abstract  

Proteins can be grouped into families according to some features such as hydrophobicity, composition or structure, aiming
to establish common biological functions. This paper presents MAHATMA—memetic algorithm-based highly adapted tool for motif
ascertainment—a system that was conceived to discover features (particular sequences of amino acids, or motifs) that occur
very often in proteins of a given family but rarely occur in proteins of other families. These features can be used for the
classification of unknown proteins, that is, to predict their function by analyzing their primary structure. Experiments were
done with a set of enzymes extracted from the Protein Data Bank. The heuristic method used was based on genetic programming
using operators specially tailored for the target problem. The final performance was measured using sensitivity, specificity
and hit rate. The best results obtained for the enzyme dataset suggest that the proposed evolutionary computation method is
effective in finding predictive features (motifs) for protein classification.

  • Content Type Journal Article
  • DOI 10.1007/s00500-010-0624-9
  • Authors
    • Denise Fukumi Tsunoda, Federal University of Parana Av. Prefeito Lothário Meissner, 632, Room 38 Curitiba PR Brazil
    • Alex Alves Freitas, University of Kent School of Computing Room S107 Canterbury Kent CT2 7NF UK
    • Heitor Silvério Lopes, Federal University of Technology Av. 7 de Setembro, 3165, Bloco D, 3° floor Curitiba PR Brazil

Weighted local sharing and local clearing for multimodal optimisation

Abstract  Local sharing is a method designed for efficient multimodal optimisation that combines fitness sharing with spatially structured
populations and elitist replacement. In local sharing, the bias towards sharing and the influence of s…

Abstract  

Local sharing is a method designed for efficient multimodal optimisation that combines fitness sharing with spatially structured
populations and elitist replacement. In local sharing, the bias towards sharing and the influence of spatial structure is
controlled by the deme (neighbourhood) size. This introduces an undesirable trade-off; to maximise the sharing effect large
deme sizes must be used, but the opposite must be true if one wishes to maximise the influence of spatial population structure.
This paper introduces two modifications to the local sharing method. The first alters local sharing so that parent selection
and fitness sharing operate at two different spatial levels; parent selection is performed within small demes, while the effect
of fitness sharing is weighted according to the distance between individuals in the entire population structure. The second
method replaces fitness sharing within demes with clearing to produce a method that we call local clearing. The proposed methods,
as tested on several benchmark problems, demonstrate a level of efficiency that surpasses that of traditional fitness sharing
and standard local sharing. Additionally, they offer a level of parameter robustness that surpasses other elitist niching
methods, such as clearing. Through analysis of the local clearing method, we show that this parameter robustness is a result
of the isolated nature of the demes in a spatially structured population being able to independently concentrate on subsets
of the desired optima in a fitness landscape.

  • Content Type Journal Article
  • DOI 10.1007/s00500-010-0612-0
  • Authors
    • Grant Dick, University of Otago Department of Information Science Dunedin New Zealand
    • Peter A. Whigham, University of Otago Department of Information Science Dunedin New Zealand

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