Optimization procedures motivated by biological analogies. The primary idea is to try to mimic the ‘survival of the fittest’ rule of genetic mutation in the development of optimization algorithms. The process begins with a population of potential solutions to a problem and a way of measuring the fitness or value of each solution. A new generation of solutions is then produced by allowing existing solutions to ‘mutate’ (change a little) or crossover (two solutions combine to produce a new solution with aspects of both). The aim is to produce new generations of solutions that have higher values.