A safe control of genetic evolution consists in preventing past errors of evolution from be­ing repeated. This could be done by keeping track of the history of evolution, but main­taining and exploiting the complete history is intractable. This paper investigates the use of machine learning (ML), in order to extract manageable information from this history. More pre­cisely, induction from examples of past trials and errors provides rules discriminating er­rors from successful trials. Such rules allow to a priori estimate the desirability of future trials; this knowledge can support powerful control strategies. SeveraI strategies of ML-based control are ap­plied to a genetic algorithm, and tested on the RoyaI Road, a GA-deceptive, and a com­binatorial optimization problem. Comparing mutation control with crossover control yields unexpected results.