Genetic algorithms: a simple R example by Bart Smeets.
From the post:
Genetic algorithm is a search heuristic. GAs can generate a vast number of possible model solutions and use these to evolve towards an approximation of the best solution of the model. Hereby it mimics evolution in nature.
GA generates a population, the individuals in this population (often called chromosomes) have a given state. Once the population is generated, the state of these individuals is evaluated and graded on their value. The best individuals are then taken and crossed-over – in order to hopefully generate ‘better’ offspring – to form the new population. In some cases the best individuals in the population are preserved in order to guarantee ‘good individuals’ in the new generation (this is called elitism).
The GA site by Marek Obitko has a great tutorial for people with no previous knowledge on the subject.
As the size of data stores increase, the cost of personal judgement on each subject identity test will as well. Genetic algorithms may be one way of creating subject identity tests in such situations.
In any event, it won’t harm anyone to be aware of the basic contours of the technique.
I first saw this at R-Bloggers.