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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.
The papers in this volume present theoretical aspects and applications of artificial neural networks and genetic algorithms. Also included are papers on fuzzy logic, soft computing, and artificial intelligence. Fundamental issues are addressed such as the nonlinear approximation capabilities of neural networks and formal methods of data representation with topological properties. New elements in genetic algorithms are presented, for example, crossover methods and gene representation. Papers on applications of neural networks show how successful these methods are in a wide range of fields like meteorological and atmospheric pollution forecasts, furnace control, and system identification. Genetic algorithms are used to solve optimization problems related to shipping and computer vision. Fuzzy-logic-based techniques are applied to sociodynamic models and hybrid neuro-fuzzy models.
The papers in this volume present theoretical insights and reports on successful applications of articifical neural networks and genetic algorithms. A dual affinity with biology is shown as several papers deal with cognition, neurocontrol, and biologically inspired brain models and others describe successful applications of computational methods to biology and environmental science. Theoretical contributions cover a variety of topics including nonlinear approximation by feedforward networks, representation of spiking perceptrons by classical ones, recursive networks and associative memories, learning and generalization, population attractors, and proposal and analysis of new genetic operators or measures. These theoretical studies are augmented by a wide selection of application-oriented papers on topics ranging from signal processing, control, pattern recognition and times series prediction to routing tasks. To keep track of the rapid development of the field of computational intelligence, the scope of the conference has been extended to hybrid methods and tools for which neural networks and evolutionary algorithms are combined with methods of soft computing, fuzzy logic, probabilistic computing, and symbolic artificial intelligence, to computer-intensive methods in control and data processing, and to data mining in meteorology and air pollution.
From the contents: Neural networks - theory and applications: NNs
(= neural networks) classifier on continuous data domains- quantum
associative memory - a new class of neuron-like discrete filters to
image processing - modular NNs for improving generalisation
properties - presynaptic inhibition modelling for image processing
application - NN recognition system for a curvature primal sketch -
NN based nonlinear temporal-spatial noise rejection system -
relaxation rate for improving Hopfield network - Oja's NN and
influence of the learning gain on its dynamics Genetic algorithms -
theory and applications: transposition: a biological-inspired
mechanism to use with GAs (= genetic algorithms) - GA for decision
tree induction - optimising decision classifications using GAs -
scheduling tasks with intertask communication onto multiprocessors
by GAs - design of robust networks with GA - effect of degenerate
coding on GAs - multiple traffic signal control using a GA -
evolving musical harmonisation - niched-penalty approach for
constraint handling in GAs - GA with dynamic population size - GA
with dynamic niche clustering for multimodal function optimisation
Soft computing and uncertainty: self-adaptation of evolutionary
constructed decision trees by information spreading - evolutionary
programming of near optimal NNs
This is the third in a series of conferences devoted primarily to
the theory and applications of artificial neural networks and
genetic algorithms. The first such event was held in Innsbruck,
Austria, in April 1993, the second in Ales, France, in April 1995.
We are pleased to host the 1997 event in the mediaeval city of
Norwich, England, and to carryon the fine tradition set by its
predecessors of providing a relaxed and stimulating environment for
both established and emerging researchers working in these and
other, related fields. This series of conferences is unique in
recognising the relation between the two main themes of artificial
neural networks and genetic algorithms, each having its origin in a
natural process fundamental to life on earth, and each now well
established as a paradigm fundamental to continuing technological
development through the solution of complex, industrial, commercial
and financial problems. This is well illustrated in this volume by
the numerous applications of both paradigms to new and challenging
problems. The third key theme of the series, therefore, is the
integration of both technologies, either through the use of the
genetic algorithm to construct the most effective network
architecture for the problem in hand, or, more recently, the use of
neural networks as approximate fitness functions for a genetic
algorithm searching for good solutions in an 'incomplete' solution
space, i.e. one for which the fitness is not easily established for
every possible solution instance.
Artificial neural networks and genetic algorithms both are areas of
research which have their origins in mathematical models
constructed in order to gain understanding of important natural
processes. By focussing on the process models rather than the
processes themselves, significant new computational techniques have
evolved which have found application in a large number of diverse
fields. This diversity is reflected in the topics which are
subjects of the contributions to this volume. There are
contributions reporting successful applications of the technology
to the solution of industrial/commercial problems. This may well
reflect the maturity of the technology, notably in the sense that
'real' users of modelling/prediction techniques are prepared to
accept neural networks as a valid paradigm. Theoretical issues also
receive attention, notably in connection with the radial basis
function neural network. Contributions in the field of genetic
algorithms reflect the wide range of current applications,
including, for example, portfolio selection, filter design,
frequency assignment, tuning of nonlinear PID controllers. These
techniques are also used extensively for combinatorial optimisation
problems.
Artificial neural networks and genetic algorithms both are areas of
research which have their origins in mathematical models
constructed in order to gain understanding of important natural
processes. By focussing on the process models rather than the
processes themselves, significant new computational techniques have
evolved which have found application in a large number of diverse
fields. This diversity is reflected in the topics which are the
subjects of contributions to this volume. There are contributions
reporting theoretical developments in the design of neural
networks, and in the management of their learning. In a number of
contributions, applications to speech recognition tasks, control of
industrial processes as well as to credit scoring, and so on, are
reflected. Regarding genetic algorithms, several methodological
papers consider how genetic algorithms can be improved using an
experimental approach, as well as by hybridizing with other useful
techniques such as tabu search. The closely related area of
classifier systems also receives a significant amount of coverage,
aiming at better ways for their implementation. Further, while
there are many contributions which explore ways in which genetic
algorithms can be applied to real problems, nearly all involve some
understanding of the context in order to apply the genetic
algorithm paradigm more successfully. That this can indeed be done
is evidenced by the range of applications covered in this volume.
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