Massively parallel algorithms from physics and biology. (English) Zbl 0981.65161
The basic idea of this (strange?) survey paper is the evolution principle. In the first part the evolution of computational fluid dynamics problems is computed from the Boltzmann equation by means of lattice kinetic methods that are extended to general partial differential equation problems. The second part discusses genetic algorithms for nonlinear parameter estimation and neural networks. The third part discusses artificial ecosystems (with genetic algorithms). All these algorithms parallelize in a natural way.
My opinion: All these (very robust) methods are extremely expensive in comparison to “direct” solution methods.
My opinion: All these (very robust) methods are extremely expensive in comparison to “direct” solution methods.
Reviewer: Willi Schönauer (Karlsruhe)
MSC:
| 65Y05 | Parallel numerical computation |
| 82C40 | Kinetic theory of gases in time-dependent statistical mechanics |
| 92D40 | Ecology |
| 92B20 | Neural networks for/in biological studies, artificial life and related topics |
| 76P05 | Rarefied gas flows, Boltzmann equation in fluid mechanics |
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
Keywords:
evolution algorithms; Boltzmann equation; genetic algorithms; parameter estimation; neural networks; artificial ecosystemsSoftware:
GenocopReferences:
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