The applied GA discussed in this section has allowed for an approach to tackle a multipollutant multieffect optimization problem in very flexible and transparent way. As it was not necessary to break the complex problem down into linear(ized) target functions, the results of the optimization are by far closer to real-world decisions of implementing a set of measures, rather than just giving reduction targets. In addition to that, computing time for assessment model runs is moderate, hence making it possible to run a fair number of scenarios to explore the effects of setting different targets. This is of particular importance, since this specific problem is marked by often synergistic, but sometimes conflicting environmental targets.
The application of GAs in ecological and environmental modeling is widespread, ranging from soil science to water quality assessment. Different applications of evolutionary computation have for instance been discussed in literature. And in the course of emerging information technologies involving distributed computing and GRID, GAs have been evaluated, in particular because of the known performance of these algorithms in poorly structured search spaces.
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