This is why the best solution found by such algorithms is called a local optimum in contrast with the actual best solution which is called a global optimum.
22.
A good training set should be representative of the problem at hand and also well-balanced, otherwise the algorithm might get stuck at some local optimum.
23.
Unfortunately, it generally takes an exponential number of improvement steps to find a local optimum even if the problem L can be solved exactly in polynomial time.
24.
:The original question was sensible, except that it should have used a term such as " local optimum " instead of " apex of evolution ".
25.
The problem is computationally difficult ( NP-hard ); however, there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum.
26.
For problems where finding an approximate global optimum is more important than finding a precise local optimum in a fixed amount of time, simulated annealing may be preferable to alternatives such as gradient descent.
27.
A convex objective function that is to be maximized, it will generally be easier to solve in the presence of a convex feasible set and any local optimum will also be a global optimum.
28.
For problems where finding the precise global optimum is less important than finding an acceptable local optimum in a fixed amount of time, simulated annealing may be preferable to alternatives such as gradient descent.
29.
Like many inference problems, the solutions to inferring the two variables together can be found, to a local optimum solution, by alternating updates of the two beliefs in a form of EM algorithm.
30.
Hill climbing by itself isn't guaranteed to find the optimum solution, just a " local optimum " ( the top of your hill may not be the highest point in the world ).
