The process of evolution. To understand the Genetic algorithm we need to first know how the process of evolution works.
For a specific population process this involves changes of genotypes continuing for some number of the next generations. That change allows to better adapt individuals in a given environment. Changes are done in the way that in each generation only the best adopted individuals can remain. Descendants of such individuals inherit good features from their parents so that next generation is better adopted. That process is repeating for numbers of generations that’s why changes are permanent and individuals are stronger.
In a Genetic algorithm there are some specific terms:

• Individual – is iteration of given instance (tasks permutation)
• Population – group of individuals, solutions in given generation
• Generation – each problem iteration in which given input population is influetnced by evolutionary operators ie. Crossover or Mutation
• Crossover - genetic operator used to vary the programming of a chromosome or chromosomes from one generation to the next. Two solutions from the population are selected and for chosen Crossover technique (PMX or OX) two new individuals are created. Descendants that’s inherit fragments of permutation from first and second parent.
• Mutation - is the operator that changes emerging individuals in a random manner eg. Two randomly selected individuals from the permutation are swapped (swap function). The mutation occurs with a predetermined probability at the beginning of the algorithm. Thanks to these two operators new individuals are created. If they are strong enough, they will be part of the input population in the next generation.