Adaptive and Natural Computing Algorithms: Proceedings of the International Conference in Coimbra, Portugal, 2005

560 Pages·2005·12.671 MB·English

by  Dominic Palmer-Brown, Miao Kang (auth.), Dr. Bernardete Ribeiro, Dr. Rudolf F. Albrecht, Dr. Andrej Dobnikar, Dr. David W. Pearson, Dr. Nigel C. Steele (eds.)

SpringerWien NewYork
Bernardete Ribeiro
Rudolf EAlbrecht
Andrej Dobnikar
David W. Pearson
Nigel C. Steele (eds.)
Adaptive and Natural
Computing Algorithms
Proceedings of the International Conference
in Coimbra, Portugal, 2005
SpringerWienNewYork
Dr. Bernardete Ribeiro
Departamento de Engenharia Informatica
Universidade de Coimbra, Coimbra, Portugal
Dr. Rudolf F. Albrecht
Institut fur Informatik
Universitat Innsbruck, Innsbruck, Austria
Dr. Andrej Dobnikar
Fakulteta za Racunalnistvo in Informatiko
Univerza v Ljubljani, Ljubljana, Slovenia
Dr. David W. Pearson
Equipe Universitaire de Recherche en Informatique
de Saint-Etienne (Groupe de Recherche de Roanne)
Institut Universitaire de Technologie de Roanne
Universite Jean Monnet, Saint-Etienne, France
Dr. Nigel C. Steele
Division of Mathematics
School of Mathematical and Information Sciences
Coventry University, Coventry, U.K.
This work is subject to copyright.
All rights are reserved, whether the whole or part of the material is concerned, specifically those of
translation, reprinting, re-use of illustrations, broadcasting, reproduction by photocopying machines or
similar means, and storage in data banks.
Product Liability: The publisher can give no guarantee for the information contained in this book. This
also refers to that on drug dosage and application thereof. In each individual case the respective user must
check the accuracy of the information given by consulting other pharmaceutical literature.
The use of registered names, trademarks, etc., in this publication does not imply, even in the absence of
a specific statement, that such names are exempt from the relevant protective laws and regulations and
therefore free for general use.
© 2005 Springer-Verlag/Wien
Printed in Austria
SpringerWienNewYork is part of Springer Science + Business Media,
springeronline.com
Camera-ready copies provided by authors and editors
Printed by Novographic Druck G.m.b.H., 1230 Wien, Austria
Printed on acid-free and chlorine-free bleached paper
SPIN 11393269
With 433 Figures
ISBN 3-211-24934-6 SpringerWienNewYork
Preface
The ICANNGA series of Conferences has been organised since 1993 and has a long history of promoting the
principles and understanding of computational intelligence paradigms within the scientific community and
is a reference for established workers in this area. Starting in Innsbruck, in Austria (1993), then to Ales in
Prance (1995), Norwich in England (1997), Portoroz in Slovenia (1999), Prague in the Czech Republic (2001)
and finally Roanne, in France (2003), the ICANNGA series has established itself for experienced workers in
the field. The series has also been of value to young researchers wishing both to extend their knowledge and
experience and also to meet internationally renowned experts.
The 2005 Conference, the seventh in the ICANNGA series, will take place at the University of Coimbra
in Portugal, drawing on the experience of previous events, and following the same general model, combining
technical sessions, including plenary lectures by renowned scientists, with tutorials.
In a rapidly advancing world, where technology and engineering change dramatically, new challenges in
modern informatics compel us to broaden the Conference themes in order to take into account new develop-
ments. Nevertheless we have kept the acronym ICANNGA, which now stands for International Conference
on Adaptive and Natural ComputiNG Algorithms
It is interesting to note that the themes of this conference have their origins in attempts to obtain
understanding of natural processes. Indeed, Nature itself seems capable of providing a source of inspiration
both for new research areas and new methodologies. Thus the focus of ICANNGA is on both the theoretical
aspects and also on the practical applications of computational paradigms inspired by adaptive and natural
processes, with special emphasis on adaptive and machine learning systems, neural networks, evolutionary
algorithms, fuzzy systems, support vector machines, biological computation and quantum computing.
Amongst the papers accepted for presentation, there are contributions reporting theoretical developments
as well as practical applications both for neural networks, genetic algorithms and evolutionary computation.
Unsupervised and clustering techniques as well as self-organising maps are also the subjects of a substan-
tial number of papers. Also, the relevance of computational neuroscience with a strong biological trend is
evidenced by the number of papers submitted. It is pleasing to observe the number of contributions report-
ing successful applications of the technology to the solution of industrial problems. Predominantly, there
are applications of neural networks to process engineering and to robotics and control which may reflect
the maturity reached by the methodology. Contributions also abound in the field of evolutionary computa-
tion particularly in combinatorial and optimisation problems. They range from evolutionary computation
theory to real-world applications, from global numerical optimisation to combinatorial optimisation, from
evolutionary learning to multi-agent systems. The topics covered by the accepted papers reflect the breadth
and depth of these fields. It is worth noting that they are inherently interdisciplinary. They interact closely
with computer science, engineering, biology, mathematics, economics, and many papers at this conference
illustrate such interdisciplinarity quite clearly.
Contributed papers in the field of natural computing, swarm optimisation and bioinformatics and compu-
tational biology are no less compelling. The awareness in the academic communities that hybrid approaches
of intelligent systems will be necessary if the real challenges in computational intelligence are to be tackled
is a fundamental stimulus to their investigation and has been acknowledged for the many papers with cross
fertilising ideas submitted. Many papers are dedicated to machine learning and heuristics and others to
soft computing applications. Some papers are devoted to the theory of computation in which traditional
information is replaced by its quantum physical counterpart showing that it is an interesting challenge to
study quantum computation. In addition, kernel based algorithms, able to solve tasks other than classifica-
tion, represent a revolution in pattern recognition bridging existing gaps. Some papers either theoretical or
application oriented are dedicated to pattern recognition, intelligent signal processing and computer vision.
We would like to express our gratitude to everyone who contributed in any way to the completion of this
volume. In particular, we thank the members of the Programme Committee for reviewing the submissions
and making the final decisions on the acceptance of papers, and the additional reviewers Mika Hirvensalo,
Jorge Tavares, Pino Buzzanca, Jose Neto, Armando Vieira and Miguel Rocha for their work. We thank also
VI
Jorge Tavares and Pedro Martins (University of Coimbra) for their difficult and time consuming task of
checking paper source files for correctness and preparing the WT^K.volume initial pages, Silvia Schilgerius
(Springer-Ver lag) and finally to all authors for their valuable research work and for their submissions.
B. Ribeiro University of Coimbra, Portugal
R. Albrecht University of Innsbruck, Austria
A. Dobnikar University of Ljubljana, Slovenia
D. W. Pearson Jean Monnet University of Saint-Etienne, France
N. C. Steele Coventry University, England
Contents
Advisory and Programme Committees XIV
Part I - Neural Networks
Neural Networks Theory I
ADFUNN: An Adaptive Function Neural Network 1
D. Palmer-Brown, M. Kang
Certain Comments on Data Preparation for Neural Networks Based Modelling 5
B. Beliczynski
A Simple Method for Selection of Inputs and Structure of Feedforward Neural Networks 9
H. Saxen, F. Pettersson
The Concept and Properties of Sigma-if Neural Network 13
M. Huh, H. Kwasnicka
Beta Wavelet Networks for Function Approximation 18
W. Bellil, C. Ben Amar, A. M. Alimi
Neural Networks Theory II
Speeding up Backpropagation with Multiplicative Batch Update Step 22
P. Cruz
Generating Sequential Triangle Strips by Using Hopfield Nets 25
J. Sima
The Linear Approximation Method to the Modified Hopfield Neural Network Parameters Analysis .. 29
5. I. Bank, S. M. Perovich, A. Lompar
The Analytical Analysis of Hopfield Neuron Parameters by the Application of Special Trans
Function Theory 33
S. M. Perovich, S. I. Bank, N. Konjevic
Time-Oriented Hierarchical Method for Computation of Minor Components 38
M. Jankovic, H. Ogawa
Learning Theory
Evolution versus Learning in Temporal Neural Networks 42
H. Soula, G. Beslon, J. Favrel
Minimization of Empirical Error over Perceptron Networks 46
V. Kurkovd
Interval Basis Neural Networks 50
A. Horzyk
Learning from Randomly-Distributed Inaccurate Measurements 54
J. Eidson, B. Hamilton, V. Kanevsky
Combining Topological and Cardinal Directional Relation Information in QSR 62
H. Sun
An Evidence Theoretic Ensemble Design Technique 66
H. Altingay
Computational Neuroscience and Neurodynamics
Cortical Modulation of Synaptic Efficacies through Norepinephrine .. 70
O. Hoshino
Associative Memories with Small World Connectivity 74
N. Davey, L. Calcraft, B. Christianson, R. Adams
VIII
A Memory-Based Reinforcement Learning Model Utilizing Macro-Actions 78
M. Murata, S. Ozawa
A Biologically Motivated Classifier that Preserves Implicit Relationship Information in Layered
Networks 82
C. C. Peck, J. Kozloski, G. A. Cecchi, A. R. Rao
Large Scale Hetero-Associative Networks with Very High Classification Ability and Attractor
Discrimination Consisting of Cumulative-Learned 3-Layer Neural Networks 86
Y. Yatsuzuka, Y. Ho
Neural Networks in Process Engineering
Crack Width Prediction of RC Structures by Artificial Neural Networks 92
C. Avila , Y. Tsuji, Y. Shiraishi
A Neural Network System for Modelling of Coagulant Dosage Used in Drinking Water Treatment ... 96
B. Lamrini, A. Benhammou, A. Karama, M. V. Le Lann
ANN Modeling Applied to NOx Reduction with Octane. ANN Future in Personal Vehicles 100
M. Ronnholm, K. Awe, K. Erdnen, F. Klingstedt, T. Salmi, H. Saxen
A Method for Detecting Cause-effects in Data from Complex Processes 104
M. Helle, H. Saxen
Predictive Data Mining on Rubber Compound Database 108
M. Trebar, U. Lotric
Neural Networks in Robotics and Control
Applying Neural Network to Inverse Kinematic Problem for 6R Robot Manipulator with Offset Wrist 112
Z. Bingul, H. M. Ertunc, C. Oysu
Local Cluster Neural Network Chip for Control 116
L. Zhang, J. Sitte, U. Rueckert
A Switching Controller for Nonlinear Systems via Fuzzy Models 120
M. Boumehraz, K. Benmahammed
Competitive Decentralized Autonomous Neural Net Controllers 124
T. Ohba, M. Ishida
Improved Hierarchical Fuzzy Control Scheme 128
T. M. Jelleli, A. M. Alimi
On-line Inference of Finite Automata in Noisy Environments 132
/. Gabrijel, A. Dobnikar
Clustering and Unsupervised Learning
Improved Clustering by Rotation of Cluster Centres 136
D. W. Pearson, M. Batton-Hubert
Hierarchical Growing Neural Gas 140
K. A. J. Doherty, R. G. Adams, N. Davey
A Clustering Algorithm Using Cellular Learning Automata Based Evolutionary Algorithm 144
R. Rastegar, M. Rahmati, M. R. Meybodi
Estimating the Number of Clusters from Distributional Results of Partitioning a Given Data Set ... 151
U. Moller
AUDyC Neural Network Using a New Gaussian Densities Merge Mechanism 155
H. A. Boubacar, S. Lecoeuche, S. Maouche
Self-Organising Maps
The Growing Hierarchical Self-Organizing Feature Maps and Genetic Algorithms for Large Scale
Power System Security -^y
M. Boudour, A. Hellal
3D Self-organizing Convex Neural Network Architectures 164
F. Boudjemai, P. B. Enberg, J. G. Postaire
IX
Novel Learning Algorithm Aiming at Generating a Unique Units Distribution in Standard SOM .... 168
K. Marzouki, T. Yamakawa
SOM-Based Estimation of Meteorological Profiles 173
T. Tambouratzis
An Efficient Heuristic for the Traveling Salesman Problem Based on a Growing SOM-like Algorithm 177
C. Garcia, J. Moreno
Part II — Evolutionary Computation
Evolutionary Computation I
Evolutionary Design and Evaluation of Modeling System for Forecasting Urban Airborne Maximum
Pollutant Concentrations 181
H. Niska, T. Hiltunen, A. Karppinen, M. Kolehmainen
Evolving Evolvability: Evolving Both Representations and Operators 185
G. W. Braught
A Multi-Objective Evolutionary Algorithm for Solving Traveling Salesman Problems: Application
to the Design of Polymer Extruders 189
A. Gaspar-Cunha
The Pareto-Box Problem for the Modelling of Evolutionary Multiobjective Optimization Algorithms 194
M. Koppen, R. Vicente-Garcia, B. Nickolay
Evolutionary Computation II
Implementation and Experimental Validation of the Population Learning Algorithm Applied to
Solving QAP Instances 198
J. Jedrzejowicz, P. Jedrzejowicz
Estimating the Distribution in an EDA 202
5. Shakya, J. McCall, D. F. Brown
Modelling Chlorine Decay in Water Networks with Genetic Programming 206
P. Jonkergouw, E. Keedwell, S.-T. Khu
Evolving Blackjack Strategies Using Cultural Learning 210
D. Curran, C. O'Riordan
Two-Criterion Optimization in State Assignment for Synchronous Finite State Machines Using
NSGA-II 214
N. Gupta, V. K. Agrawal
Genetic Algorithms Theory
Offspring Selection: A New Self-Adaptive Selection Scheme for Genetic Algorithms 218
M. Affenzeller, S. Wagner
Using Genetic Algorithms with Real-coded Binary Representation for Solving Non-stationary Problems 222
J. Kubalik
Dynamics in Proportionate Selection 226
A. Agrawal, I. Mitchell, P. Passmore, I. Litovski
Generating Grammatical Plant Models with Genetic Algorithms 230
L. E. Da Costa, J.-A. Landry
Complex Systems and Genetic Programming
Golomb Rulers: Experiments with Marks Representation 235
J. Tavares, F. B. Pereira, E. Costa
Evolving Segments Length in Golomb Rulers 239
J. Tavares, T. Leitao, F. B. Pereira, E. Costa
Resource-Limited Genetic Programming: Replacing Tree Depth Limits . 243
S. Silva, P. J. N. Silva, E. Costa
X
Treating Some Constraints as Hard Speeds up the ESG Local Search Algorithm 247
Y. Kilani, A. Mohdzin
Swarm Optimization
Applications of PSO Algorithm and OIF Elman Neural Network to Assessment and Forecasting for
Atmospheric Quality 251
L. M. Wang, X. H. Shi, M. Li, G. J. Chen, H. W. Ge, H. P. Lee, Y. C. Liang
A Hybrid Particle Swarm Optimization Model for the Traveling Salesman Problem 255
T. R. Machado, H. S. Lopes
Perceptive Particle Swarm Optimisation 259
B. Kaewkamnerdpong, P. J. Bentley
Wasp Swarm Optimization of Logistic Systems 264
P. Pinto, T. A. Runkler, J. M. Sousa
A Parallel Vector-Based Particle Swarm Optimizer 268
/. L Schoeman, A. P. Engelbrecht
Molecular and Quantum Computing
Numerical Simulations of a Possible Hypercomputational Quantum Algorithm 272
A. Sicard, J. Ospina, M. Velez
Simulated Fault Injection in Quantum Circuits with the Bubble Bit Technique 276
M. Udrescu, L. Prodan, M. Vldduiiu
Redundant Quantum Arithmetic 280
A. Pereira, R. Rodrigues
A Special Class of Additive Cyclic Codes for DNA Computing 284
T. Abualrub, A. Ghrayeb, X. Zeng
Part III — Adaptive and Natural Computing
Biolnformatics and Computational Biology I
Evolutionary Algorithms for Static and Dynamic Optimization of Fed-batch Fermentation Processes. 288
M. Rocha, J. Neves, A. C. A. Veloso, E. C. Ferreira, I. Rocha
Benchmark Testing of Simulated Annealing, Adaptive Random Search and Genetic Algorithms for
the Global Optimization of Bioprocesses 292
R. Oliveira, R. Salcedo
Dynamic Modelling and Optimisation of a Mammalian Cells Process Using Hybrid Grey-box Systems 296
A. Teixeira, A. Cunha, J. Clemente, P. M. Alves, M. J. T. Carrondo, R. Oliveira
Adaptive DO-based Control of Substrate Feeding in High Cell Density Cultures Operated under
Oxygen Transfer Limitation 300
R. Oliveira, A. Cunha, J. Clemente, M. J. T. Carrondo
Evolutionary Design of Neural Networks for Classification and Regression 304
M. Rocha, P. Cortez, J. Neves
Biolnformatics and Computational Biology II
Pelican - Protein-structure Alignment Using Cellular Automaton Models 308
D. K. Gangadhar
An Efficient Algorithm for De Novo Peptide Sequencing 312
S. Brunetti, D. Dutta, S. Liberatori, E. Mori, D. Varrazzo
Emergent Behavior of Interacting Groups of Communicative Agents 316
A. Bisler
Integrating Binding Site Predictions Using Meta Classification Methods 321
Y. Sun, M. Robinson, R. Adams, A. G. Rust, P. Kaye, N Davey
XI
Reverse Engineering Gene Networks with Artificial Neural Networks 325
A. Krishna, A. Narayanan, E. C. Keedwell
Adaptive Technology in AI
A Meta-Level Architecture for Adaptive Applications 329
F. J. Barth, E. S. Gomi
Adaptive Finite State Automata and Genetic Algorithms: Merging Individual Adaptation and
Population Evolution 333
H. Piston, P. 5. Martins, A. A. de Castro Jr.
Modeling a Tool for the Generation of Programming Environments for Adaptive Formalisms 337
A. R. Camolesi
Adaptive Decision Tables: A Case Study of Their Application to Decision-Taking Problems 341
T. C. Pedrazzi, A. E. Tchemra, R. L. A. Rocha
Robotic Mapping and Navigation in Unknown Environments Using Adaptive Automata 345
M. A. de A. de Sousa, A. R. Hirakawa
An Adaptive Framework for the Design of Software Specification Languages 349
J. J. Neto, P. S. M. Silva
Natural Computing
Swarm Intelligence Clustering Algorithm Based on Attractor 353
Q. Li, Z. Shi, Z. Shi
Ant-based Distributed Optimization for Supply Chain Management 357
C. A. Silva, J. M. Sousa, T. Runkler, J. M. G. Sa da Costa
Comparison of Nature Inspired and Deterministic Scheduling Heuristics Considering Optimal
Schedules 361
U. Honig, W. Schiffmann
An External Memory Supported ACO for the Frequency Assignment Problem 365
A. Acan, A. Gunay
Part IV - Soft Computing Applications
Machine Learning and Heuristics I
Neural Networks for Extraction of Fuzzy Logic Rules with Application to EEG Data .............. 369
M. Holena
A New Neuro-Based Method for Short Term Load Forecasting of Iran National Power System 373
R. Barzamini, M. B. Menhaj, S. Kamalvand, M. A. Fasihi
Approximating the Algebraic Solution of Systems of Interval Linear Equations with Use of Neural
Networks 377
N. H. Viet, M. Kleiber
Comparing Diversity and Training Accuracy in Classifier Selection for Plurality Voting Based Fusion 381
H. Altingay
Machine Learning and Heuristics II
Visualization of Meta-Reasoning in Multi-Agent Systems 385
D. Rehof, J. Tozicka, P. Slavik
Intelligent Agent Inspired Genetic Algorithm 389
C. G. Wu, Y. C. Liang, H. P. Lee, C Lu
Combining Lazy Learning, Racing and Subsampling for Effective Feature Selection 393
G. Bontempi, M. Birattari, P. E. Meyer
Personalized News Access 397
D. G. Kaklamanos, K. G. Margaritis
A More Accurate Text Classifier for Positive and Unlabeled Data 401
R.-M. Xin, W.-L. Zuo