The genetic algorithm is an optimization tool that mimics the process of evolution through genetics. The parameters to be optimized are the genes, which are strung together in an array called the chromosome. A population of chromosomes is created and evaluated by the cost function, with the “most fit” chromosomes being kept in the population while the “least fit” ones are discarded. The chromosomes are then paired so they can mate, involving combining portions of each chromosome to produce new chromosomes. Mutations are imposed. The new chromosomes are evaluated by the cost function and the process iterates. Thus the parameter space is explored by a combination of combining parts of the best solutions as well as extending the search through mutations. The trade-offs involved in selecting population size, mutation rate, and mate selection will be briefly discussed.
The key to using GAs in environmental sciences is to pose the problem as one in optimization. Many problems are quite naturally optimization problems, such as the many uses of inverse models in environmental science. Other problems can be manipulated into optimization form by careful definition of the cost function, so that even nonlinear differential equations can be approached using GAs. Examples of both the natural type as well as those contrived into an optimization form are presented.