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How do you overcome the local minimum?
In statistical physics, particles have average speed v, where the average temperature is such that v =sqrt(3kT/m). You increase the temperature to get particles moving, in particular this causes the system to get out of local minima traps.
Can logistic regression get stuck in local minima?
Since Logistic Regression Model cost function is convex, there is no local minimum. It can also only reach the local minimum based on where the initialization is. Suppose you use Batch Gradient Descent and you plot the validation error at every epoch.
What is the local minima problem?
A local minimum is a suboptimal equilibrium point at which system error is non-zero and the hidden output matrix is singular [12]. The complex problem which has a large number of patterns needs as many hidden nodes as patterns in order not to cause a singular hidden output matrix.
How to overcome strong local minima in genetic algorithm?
Fixing another subset will allow to find better solution. Imagine that you are searching minimum of f (x,y) = x2 + y2 with fixed x=x0 (with x0 not equal to zero!), then with fixed y=y0 (again y0 is not equal to zero). But there might be two other reasons of your problems.
How to avoid getting stuck on local optimum?
Redesign your genetic operations. e.g. allow bad fitness individuals/offspring to survive the generation more frequently. It could be that you’re currently selecting too much good individuals to survive.
When to use techniques as if they were steps of the algorithm?
You also describe techniques as if it were a step of the algorithm (e.g. Elitism is a technique used in the recombination step to ensure that at least the best individuals don’t die).
How to avoid getting stuck on local optimum, for genetic?
When results stop changin for some number of iterations – this is when you stop, so it is also the moment to start the new search. I would propose to mix GA with SA to find global optimum. Working hacky solution is to remember local optima and restart (mutate or reinitialize), but after it discarded attractor – drop mutation rate.