Here we continue with a comparison between the sequential simplex method and genetic algorithms. Both these techniques are in fact inspired by the biological evolution. Genetic algorithms are operating with even more similarity to natural selection and evolution. Mutations are also introduced at a low preset frequency. The simplex method provides a quick search and convergence with few trials.
Genetic algorithms are slower and require many more trials.
Genetic algorithms are therefore not a choice that we can recommend for experimental optimization. Genetic algorithms have however found other applications. The slow search and convergence is not a big problem in numerical optimization with today's number crunching capacity. Genetic algorithms provide an efficient global search and can handle very complex numerical optimization problems. This also means that a genetic algorithm can avoid getting caught on local optima.