Jordi Verbruggen and Jietse Verweirder
Genetic algorithms (GAs) are described as optimization algorithms that make use of concepts originated from biological evolution, such as natural selection and survival of the fittest. By introducing these concepts into the algotithm, a population will evolve to an optimal solution. The population is a representation of possible solutions, called chromosomes, to the problem in question. Each chromosome in their turn is comprised of genes, which are the actual number values of the solution. The algorithm will then apply evolutionary operators such as mutation, recombination and selection, over several generations, to converge the population to the best possible solution. A fitness function is used to determine which solution is the most optimal in the population, determining the objective function quality of the solution. Every generation, better solutions are created until the termination criteria are met or until a certain amount of generations has been run. The GA is terminated and the optimal solution should have been found. Although not guaranteed, most solutions will be of sufficient high quality.