10.10 Summary

GAs can even be faster in finding global maxima than conventional methods, in particular when derivatives provide misleading information. We should not forget, however, that, in most cases where conventional methods can be applied, GAs are much slower because they do not take auxiliary information like derivatives into account. In these optimization problems, there is no need to apply a GA which gives less accurate solutions after much longer computation time. The enormous potential of GAs lies elsewhere in optimization of non-differentiable or even discontinuous functions, discrete optimization, and program induction. Thus due to these reasons genetic algorithm is found to be used in a variety of applications as discussed in this chapter. Apart from these applications dealt in this chapter, GAs can be applied to production planning, air traffic problems, automobile, signal processing, communication networks, environmental engineering and so on.