Artificial Neural Nets and Genetic Algorithms

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.