Genetic algorithms, genetic programming and evolutionary computing are terms that fall within the general sphere of `Evolutionary Computation`. Genetic algorithms (GAs) are computationally intensive global search heuristics with very wide applicability, but their implementation is often highly problem specific and there is only a relatively loose understanding as to why they often work rather well. The central idea behind GAs is to mimic the Darwinian notion that selective breeding seeks optimum individuals in a given environment. In order to do this a GA requires a way of representing a solution to a (spatial) problem as if it were an individual viewed as a chromosome or `genome` like object. The aim of the topic is to provide fundamental understanding of the principles behind genetic algorithms, and its application in solving geospatial problems.
In progress (GI-N2K)