In a similar manner, genetic algorithms can be viewed as performing stochastic hill-climbing, which is again a subset of a wider class of algorithms for optimization. Each of these algorithms for building classifiers or for searching a solution space has its own profile of strengths and weaknesses which can be studied mathematically.
