Search Space

If have solving some problem, usually looking for some solution, which will be the best among others. The space of all feasible solutions (it means objects among those the desired solution is) is called search space. Each point in the search space represents one feasible solution. Each feasible solution can be "marked" by its value or fitness for the problem. Looking for a solution, which is one point (or more) among feasible solutions that are one point in the search space.

The looking for a solution is then equal to a looking for some extreme (minimum or maximum) in the search space. The search space can be whole known by the time of solving a problem, but usually we know only a few points from it and we are generating other points as the process of finding solution continues.

The problem is that the search can be very complicated. One does not know where to look for the solution and where to start. There are many methods, how to find some suitable solution (i.e. not necessarily the best solution), for example hill climbing, tabu search, simulated annealing and genetic algorithm. The solution found by this method is often considered as a good solution, because it is not often possible to prove what the real optimum is.