Genetic/Evolutionary algorithms and local minima/maxima

Question

I have run across several posts and articles that suggests using things like simulated annealing to avoid the local minima/maxima problem.

I don't understand why this would be necessary if you started out with a sufficiently large random population.

Is it just another check to insure that the initial population was, in fact, sufficiently large and random? Or are those techniques just an alternative to producing a "good" initial population?

Answer 1

Simulated annealing is a probabilistic optimization technique -- it's not supposed to give you more precise answers, it's supposed to give you approximations faster.

Answer 2

Simulated annealing is probabilistic technique where chance of getting trapped in local minima/maxima depends on scheduling of temperature. Scheduling temperature is different for different types of problems. Evolutionary Algorithm is much more robust and less likely to get trapped in local minima/maxima. SA is probabilistic. On the other hand, EA uses mutation which introduces random walk in search space, that's why EA has higher probability of getting global optima.

Answer 3

First of all, simulated annealing is a last resort method. There are far better, more efficient, and more effective methods of discovering where the local minima are found.

A better check would be to use a statistical method to uncover information about your data set such as variance or standard deviation.