5.4.1 Genetic Optimization Algorithms

Genetic Algorithms (GA) were first introduced by HOLLAND [99]. GAs are so-called population based search strategies. They maintain a set of points, so-called genomes, in a function space and try to make use of analogies to biological evolution by performing mutation and crossover operations between the individuals of a population. Starting with an initial population, the algorithm evolves by iteratively creating a new generation of individuals based on an already existing one. New individuals are introduced by a so-called crossover operation, where at least two5.4individuals of a generation are chosen as 'parents'. Their genomes are combined to produce children. On some of the newly generated children a so-called mutation operation is performed by replacing a part of the child with a random value. Depending on the genetic algorithm at hand some individuals of the parental generation might survive into the following generation(s). Individuals are chosen for mating based on their fitness. The fitness is a metric to indicate how good an individual represents a solution of the problem. It directly corresponds to the target value of a gradient based optimizer. When the optimization is started an initial population of genomes is chosen. The parameters of the genomes are initialized randomly but within given bounds. The fitness of the population is then computed by evaluating each individual by means of a simulation run.