Category: Uncategorized

History mechanism supported differential evolution for chess evaluation function tuning

Abstract  This paper presents a differential evolution (DE) based approach to chess evaluation function tuning. DE with opposition-based
optimization is employed and upgraded with a history mechanism to improve the evaluation of individuals …

Abstract  

This paper presents a differential evolution (DE) based approach to chess evaluation function tuning. DE with opposition-based
optimization is employed and upgraded with a history mechanism to improve the evaluation of individuals and the tuning process.
The general idea is based on individual evaluations according to played games through several generations and different environments.
We introduce a new history mechanism which uses an auxiliary population containing good individuals. This new mechanism ensures
that good individuals remain within the evolutionary process, even though they died several generations back and later can
be brought back into the evolutionary process. In such a manner the evaluation of individuals is improved and consequently
the whole tuning process.

  • Content Type Journal Article
  • Pages 1-17
  • DOI 10.1007/s00500-010-0593-z
  • Authors
    • B. Bošković, Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova ul. 17, 2000 Maribor, Slovenia
    • J. Brest, Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova ul. 17, 2000 Maribor, Slovenia
    • A. Zamuda, Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova ul. 17, 2000 Maribor, Slovenia
    • S. Greiner, Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova ul. 17, 2000 Maribor, Slovenia
    • V. Žumer, Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova ul. 17, 2000 Maribor, Slovenia

Covariance matrix self-adaptation evolution strategies and other metaheuristic techniques for neural adaptive learning

Abstract  A covariance matrix self-adaptation evolution strategy (CMSA-ES) was compared with several metaheuristic techniques for multilayer
perceptron (MLP)-based function approximation and classification. Function approximation was based o…

Abstract  

A covariance matrix self-adaptation evolution strategy (CMSA-ES) was compared with several metaheuristic techniques for multilayer
perceptron (MLP)-based function approximation and classification. Function approximation was based on simulations of several
2D functions and classification analysis was based on nine cancer DNA microarray data sets. Connection weight learning by
MLPs was carried out using genetic algorithms (GA–MLP), covariance matrix self-adaptation-evolution strategies (CMSA-ES–MLP),
back-propagation gradient-based learning (MLP), particle swarm optimization (PSO–MLP), and ant colony optimization (ACO–MLP).
During function approximation runs, input-side activation functions evaluated included linear, logistic, tanh, Hermite, Laguerre,
exponential, and radial basis functions, while the output-side function was always linear. For classification, the input-side
activation function was always logistic, while the output-side function was always regularized softmax. Self-organizing maps
and unsupervised neural gas were used to reduce dimensions of original gene expression input features used in classification.
Results indicate that for function approximation, use of Hermite polynomials for activation functions at hidden nodes with
CMSA-ES–MLP connection weight learning resulted in the greatest fitness levels. On average, the most elite chromosomes were
observed for MLP (

MSE=0.4977

), CMSA-ES–MLP (0.6484), PSO–MLP (0.7472), ACO–MLP (1.3471), and GA–MLP (1.4845). For classification analysis, overall average
performance of classifiers used was 92.64% (CMSA-ES–MLP), 92.22% (PSO–MLP), 91.30% (ACO–MLP), 89.36% (MLP), and 60.72% (GA–MLP).
We have shown that a reliable approach to function approximation can be achieved through application of MLP connection weight
learning when the assumed function is unknown. In this scenario, the MLP architecture itself defines the equation used for
solving the unknown parameters relating input and output target values. A major drawback of implementing CMSA-ES into an MLP
is that when the number of MLP weights is large, the

O(N3)

Cholesky factorization becomes a bottleneck for performance. As an alternative, feature reduction using SOM and NG can greatly
enhance performance of CMSA-ES–MLP by reducing

N.

Future research into the speeding up of Cholesky factorization for CMSA-ES will be helpful in overcoming time complexity
problems related to a large number of connection weights.

  • Content Type Journal Article
  • Pages 1-13
  • DOI 10.1007/s00500-010-0598-7
  • Authors
    • Leif E. Peterson, Center for Biostatistics, The Methodist Hospital Research Institute, Houston, TX 77030, USA

Hedges and successors in basic algebras

Abstract  The concept of hedge was introduced by Zadeh in the sake to amplify true values of linguistic terms. It was used by Bělohlávek
and Vychodil for formal concept analysis of unsharp reasoning. The concept of successor was introduced…

Abstract  

The concept of hedge was introduced by Zadeh in the sake to amplify true values of linguistic terms. It was used by Bělohlávek
and Vychodil for formal concept analysis of unsharp reasoning. The concept of successor was introduced by Caicedo and Cignoli
for study of intuitionistic connectives and used by San Martín, Castiglioni, Menni and Sagastume in Heyting algebras. Since
basic algebras form an algebraic tool for simultaneous treaty of many-valued logics and logics of quantum mechanics, it arises
a natural question of generalization of these concepts also for basic algebras. This motivated our investigations on hedges
and successors.

  • Content Type Journal Article
  • Pages 1-6
  • DOI 10.1007/s00500-010-0570-6
  • Authors
    • Ivan Chajda, Palacký University Olomouc Department of Algebra and Geometry, Faculty of Sciences třída 17. listopadu 12 771 46 Olomouc Czech Republic

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

Validating criteria with imprecise data in the case of trapezoidal representations

Abstract  We are interested in the issue of determining an alternative’s satisfaction to a criterion when the alternative’s associated
attribute value is imprecise. We introduce two approaches to the determination of criteria satisfactio…

Abstract  

We are interested in the issue of determining an alternative’s satisfaction to a criterion when the alternative’s associated
attribute value is imprecise. We introduce two approaches to the determination of criteria satisfaction in this uncertain
environment, one based on the idea of containment and the other on the idea of possibility. We are particularly interested
in the case in which the imprecise data is expressed in terms of a trapezoidal type distribution. We provide an algorithmic
solution to this problem enabling it to be efficiently implemented in a digital environment. A number of examples are provided
illustrating our algorithms.

  • Content Type Journal Article
  • Pages 1-12
  • DOI 10.1007/s00500-010-0569-z
  • Authors
    • Ronald R. Yager, Iona College Machine Intelligence Institute New Rochelle NY 10801 USA