Minimum Number of Variables for Effective use of the Genetic Algorithm.

From my understanding of the genetic algorithm the population consists of individuals, where each individual is a potential solution made up of "genes", and each gene is a variable. So for a cost function that takes in 3 variables the individuals will have 3 genes.

The optimization happens by selection (picking the fittests from the population), crossover (swapping genes between pairs in the population), and mutation (randomly changing certain genes).

Because of this it seems to me that the genetic algorithm is best suited to high dimensionality optimisation problems, i.e ones with lots of variables, so that there are more genes that can be swapped and mutated.

Is this true? and what is a good number of genes for use in the genetic algorithm?

Thanks!

Answer 1

[Alan Weiss]

For a description of the genetic algorithm in Global Optimization toolbox, see the documentation.

In fact, ga is not necessarily the best solver in any dimension. For smooth problems, fminunc or fmincon are usually much faster and more reliable. For nonsmooth problems, patternsearch is usually faster and more reliable. See the documentation on how to choose a solver.

There may be some situations where ga is preferable. I don't know how these situations relate to the dimension of the problem.

Alan Weiss

MATLAB mathematical toolbox documentation