Canonical Genetic Algorithm:

In such a scenario with all search techniques there one of the first questions to ask along with GAs is how to define a search space that is actually potentially contains good solutions to the problem at hand. Because answering the question of how to represent possible solutions to the problem. But there the classical approach to GAs is to represent the solutions as strings of ones and zero as there in example, bit strings. Further this is not such a bad idea that given to computers store everything as bit strings so then any  solution would ultimately boil down to a string of ones and zeros. Moreover,, there have been many modifications to the original approach to genetic algorithms so and GA approaches here now come in many different shapes and sizes like on higher level representations. Definitely it's possible to see genetic programming, whether the individuals in the population are programs in such a scenario just a GA approach with a more complicated representation scheme. 

However returning to the classical approach, as there example, whether solving a particular problem involved finding a set of five integers between 1 and 100 after then the search space for a GA would be bits strings when the first eight bits are decoded as the first integer so the next eight bits become the second integer and so on. Therefore representing the solutions is one of the tricky parts to using genetic algorithms hence a problem we come back to later. Thus by assuming that the solutions are represented such as strings of length L.