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140122 ||| eng |
| 020 |
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|a 9783709175330
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| 100 |
1 |
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|a Albrecht, Rudolf F.
|e [editor]
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| 245 |
0 |
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|a Artificial Neural Nets and Genetic Algorithms
|h Elektronische Ressource
|b Proceedings of the International Conference in Innsbruck, Austria, 1993
|c edited by Rudolf F. Albrecht, Colin R. Reeves, Nigel C. Steele
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| 250 |
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|a 1st ed. 1993
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| 260 |
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|a Vienna
|b Springer Vienna
|c 1993, 1993
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| 300 |
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|a XIII, 737 p. 403 illus
|b online resource
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| 505 |
0 |
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|a Workshop Summary -- Artificial Neural Networks -- Genetic Algoritms -- Artificial Neural Networks & Genetic Algorithms
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| 653 |
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|a Statistics, general
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| 653 |
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|a Pattern recognition
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| 653 |
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|a Image Processing and Computer Vision
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| 653 |
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|a Pattern Recognition
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| 653 |
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|a Statistics
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| 653 |
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|a Algorithms
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| 653 |
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|a Algorithms
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Optical data processing
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| 653 |
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|a Probability Theory and Stochastic Processes
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| 653 |
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|a Probabilities
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| 700 |
1 |
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|a Reeves, Colin R.
|e [editor]
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| 700 |
1 |
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|a Steele, Nigel C.
|e [editor]
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-7091-7533-0?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 006.3
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| 520 |
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|a 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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