Description
Genetic algorithms can be used to search for good solutions to optimization problems. This helps users explore parameter spaces where direct formulas or gradient methods are difficult to apply.
It is an Octave optimization extension. Results can vary by random seed, constraints, and fitness function, so repeated validation is important.