Artificial Neural Nets and Genetic Algorithms

Workshop Summary.- Invited Talk.- Kohonen neural networks for machine and process condition monitoring.- Plenary Session.- Process modelling and control with neural networks: present status and future directions.- Session A1: Classification.- A genetic algorithm for multicriteria inventory classification.- Optimizing classifiers for handwritten digits by genetic algorithms.- DCS: A promising classifier system.- Application of neural networks for classification of temperature distribution patterns.- Session B1: Genetic Algorithms and Combinatorial Optimisation.- Combination of genetic algorithms and CLP in the vehicle-fleet scheduling problem.- Minimum cost topology optimisation of the COST 239 European optical network.- Timetabling using genetic algorithms.- Resolution of cartographic layout problem by means of improved genetic algorithms.- Using genetic algorithms to solve the radio link frequency assignment problem.- Session C1: Learning and Training.- A transformation for implementing efficient dynamic backpropagation neural networks.- AA1*: a dynamic incremental network that learns by discrimination.- Non-supervised sensory-motor agents learning.- Functional equivalence and genetic learning of RBF networks.- Teaching relaxation labeling processes using genetic algorithms.- VLSI optimal neural network learning algorithm.- Session A2: Applications in Biology and Biotechnology.- Using of neural-network and expert-system for imissions prediction.- The costing of process vessels using neural networks.- Neural network modelling of fermentation taking into account culture memory.- Brain electrographic state detection using combined unsupervised and supervised neural networks.- Session B2: Genetic and Neural Theory Combined.- From the chromosome to the neural network.- CAM-Brain: the evolutionary engineering of a billion neuron artificial brain by 2001 which grows/evolves at electronic speeds inside a cellular automata machine (CAM).- A perfect integration of neural networks and evolutionary algorithms.- G-LVQ, a combination of genetic algorithms and LVQ.- Evolving neural network structures: an evaluation of encoding techniques.- Session C2: Failure Detection and Identification.- Application of radial basis function networks to fault diagnosis for a hydraulic system.- Optimally robust fault diagnosis using genetic algorithms.- Development of a neural-based diagnostic system to control the ropes of mining shifts.- Lime kiln fault detection and diagnosis by neural networks.- Investigations into the use of wavelet transformations as input into neural networks for condition monitoring.- Session A3: Image, Motion and Recognition.- Genetic algorithm for neurocomputer image recognition.- Feature map architectures for pattern recognition: techniques for automatic region selection.- Adaptive genetic algorithms for multi-point path finding in artificial potential fields.- Spatio-temporal mask learning: application to speech recognition.- Artificial neural networks for motion estimation.- Advanced neural networks methods for recognition of handwritten characters.- Session B3: Genetic Algorithms Theory.- The use of a variable length chromosome for permutation manipulation in genetic algorithms.- Theoretical bounds for genetic algorithms.- An argument against the principle of minimum alphabet.- Heterogeneous co-evolving parasites.- Typology of boolean functions using Walsh analysis.- Session C3: Neural Networks Theory.- Artificial neural networks for nonlinear projection and exploratory data analysis.- Generic back-propagation in arbitrary feedforward neural networks.- From prime implicants to modular feedforward networks.- Hierarchical backward search method: a new classification tree using preprocessing by multilayer neural network.- Adaptation algorithms for 2-D feedforward neural networks.- Selecting the best significant fragment to the incremental heteroassociative neural network (RHI).- Session A4: Image, Motion and Recognition.- An orthogonal neural network with guaranteed recall by iterative filters and its application to texture discrimination.- Automatic radar target identification using neural networks.- Keystroke dynamics based user authentication using neural networks.- Application of learning to learn to real-world pattern recognition.- Quarry aggregates: a flexible inspection method utilising artificial neural networks.- Session B4: Genetic Algorithms Theory.- An evolution model for integration problems.- Convergence of algorithm and the schema theorem in genetic algorithms.- Incorporating neighbourhood search operators into genetic algorithms.- Automatic change of representation in genetic algorithms.- Dominant takeover regimes for genetic algorithms.- Interactive evolutionary algorithms in design.- Session C4: Neural Networks Theory.- Decrypting neural network data: a GIS case study.- A framework for creating societies of agents.- Adaptive scaling of codebook vectors.- Bias estimation for neural network predictions.- ANN realizations of local approximation schemes.- Changing network weights by Lie groups.- Session A5: Time Series, Sequences and Filters.- Unsupervised learning of temporal sequences by neural networks.- Multivariate time series modeling of financial markets with artificial neural networks.- Neural network approach to Pisarenko’s frequency estimation.- Rounding FIR filter coefficients using genetic algorithms.- Evolutionary adaptive filtering.- Session B5: Genetic Algorithms and Combinatorial Optimisation.- A genetic algorithm for some discrete-continuous scheduling problems.- Hybridizing genetic algorithms with branch and bound techniques for the resolution of the TSP.- Performance of genetic algorithm used for analysis of call and service processing in telecommunications.- Evolutionary heuristics for the bin packing problem.- A clausal genetic representation and its evolutionary procedures for satisfiability problems.- Session C5: Parallelism and Neural Networks.- Massively parallel training of multi layer perceptrons with irregular topologies.- Parallel Boltzmann machine topologies for simulated annealing realisation of combinatorial problems.- Radial Basis Function Networks.- Radial basis function neural networks in credit application vetting systems.- A dynamical architecture for a radial basis function network.- Centre selection for radial basis function networks.- Session A6: Multimedia, CAD and Software Tools.- Flexible user-guidance in multimedia CBT-applications using artificial neural networks.- Use of genetic and neural technologies in oil equipment computer-aided design.- GENESIS: an object-oriented framework for simulation of neural network models.- NNDT — a neural network development tool.- Session B6: Genetic and Neural Theory Combined.- Genetic synthesis of task-oriented neural networks.- Evolving neural networks using the “Baldwin effect”.- Genetic Algorithms Theory.- Modification of Holland’s reproductive plan for diploid populations.- Session C6: Optimisation.- Simulated annealing artificial neural networks for the satisfiability (SAT) problem.- Policy optimization by neural network and its application to queueing allocation problem.- Comparison of genetic and other unconstrained optimization methods.- Neural networks for dynamical crop growth model reduction and optimization.- Session A7: Self-Organizing Maps and Auto-Associative Memory.- Modified Kohonen’s learning laws for RBF network.- Kohonen’s maps for contour and “region-like” segmentation of gray level and color images.- Using genetic algorithm in self-organizing map design.- Modular labeling RAAM.- Self-organizing maps for supervision in robot pick-and-place operations.- Session B7: Parallelism and Genetic Algorithms.- Q-Learning and parallelism in evolutionary rule based systems.- Comparing parallel tabu search and parallel genetic algorithms on the task allocation problem.- Distributed genetic algorithms with an application to portfolio selection problems..- Optimisation.- Interval arithmetic and genetic algorithms in global optimization.- A genetic algorithm for minimization of fixed polarity Reed-Muller expressions.- Session C7: Control Theory Applications.- Identification and adaptive control of nonlinear processes using combined neural networks and genetic algorithms.- Elevator group control using distributed genetic algorithms.- Neural control of nonlinear non-minimum phase dynamical systems.- Industrial kiln multivariable control: MNN and RBFNN approaches.- Genetic tuning of neural non-linear PID controllers.- Session A8: Applications in Biology and Biotechnology.- Connectionist algorithm for a 3D dense image building from stereoscopy.- A unified neural network model for the self-organization of topographic receptive fields and lateral interaction.- A neural network implementation for an electronic nose.- Genetic algorithms (GAs) in the role of intelligent regional adaptation agents for agricultural decision support systems.- Genetic algorithm applied to radiotherapy treatment planning.- Session B8: Learning and Training.- An experience based competitive learning neural model for data compression.- Using neural networks for generic strategic planning.- Neural network training compared for backprop, quickprop and cascor in energy control problems.- Session C8: Mixed Applications.- Application of neural network for home security system.- Application of genetic algorithms in sliding mode control design.- Application of temporal neural networks to source localisation.- Using genetic algorithms for optimal design of axially loaded non-prismatic columns.- Behaviour learning by a reward-penalty algorithm: from gait learning to obstacle avoidance by neural networks.- Session A9: Training Data Selection.- Using evolutionary computation to generate training set data for neural networks.- Optimal training pattern selection using a cluster-generating artificial neural network.- Training set selection in neural network applications.- Session B9: Parallelism and Genetic Algorithms.- A genetic algorithm for load balancing in parallel query evaluation for deductive relational databases.- Combining distributed populations and periodic centralized selections in coarse-grain parallel genetic algorithms.- Modified genetic algorithms by efficient unification with simulated annealing.- VLSI standard-cell placement by parallel hybrid simulated-annealing and genetic algorithm.- Genetic Algorithms and Combinatorial Optimisation.- Performance of genetic algorithms in the solution of permutation flowshop problems.- A genetic algorithm for the maximal clique problem.- Session C9: Fuzzy Logic and Uncertainty.- Minimal error rate classification in a non-stationary environment via a modified fuzzy ARTMAP network.- Decision making in uncertain environment with genetic algorithms.- The use of fuzzy ARTMAP to identify low risk coronary care patients.- NeuroGraph — a simulation environment for neural networks, genetic algorithms and fuzzy logic.- Time Series, Sequences and Filters.- Comparison of identification techniques for nonlinear systems.