Artificial Neural Nets and Genetic Algorithms
Proceedings of the International Conference in Portoro¿, Slovenia, 1999
Herausgegeben: Dobnikar, Andrej; Steele, Nigel C.; Pearson, David W.
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From the contents:
- Neural networks - theory and applications: NN (= neural network) classifier on continuous data domain
- Quantum associative memory
- A new class of neuron-like discrete filters for image processing
- Modular NNs for improving generalization properties
- Presynaptic inhibition modelling for image processing application
- NN recognition system for a curvature primal sketch
- NNs based nonlinear temporal-spatial 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 mechanism to use with GAs (= genetic algorithms)
- GA for decision tree induction
- Optimising decision classifications with GAs
- Scheduling tasks with intertask communication onto multiprocessors by GAs
- Designing 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.
- Neural networks - theory and applications: NN (= neural network) classifier on continuous data domain
- Quantum associative memory
- A new class of neuron-like discrete filters for image processing
- Modular NNs for improving generalization properties
- Presynaptic inhibition modelling for image processing application
- NN recognition system for a curvature primal sketch
- NNs based nonlinear temporal-spatial 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 mechanism to use with GAs (= genetic algorithms)
- GA for decision tree induction
- Optimising decision classifications with GAs
- Scheduling tasks with intertask communication onto multiprocessors by GAs
- Designing 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.
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