Now both the xgboost and keras individuals are compatible with both versions of the genetic algorithm. For this, we distinguish between the reproduce method (generates a new "child" without altering parents) and the crossover method (which swaps genes between individuals, altering the genes of both parents). Now, the genetic algorithms maximize the fitness by default instead of minimizing it, which is the expected behavior. This can be undone with the maximize parameter.
Now both the xgboost and keras individuals are compatible with both versions of the genetic algorithm. For this, we distinguish between the reproduce method (generates a new "child" without altering parents) and the crossover method (which swaps genes between individuals, altering the genes of both parents). Now, the genetic algorithms maximize the fitness by default instead of minimizing it, which is the expected behavior. This can be undone with the maximize parameter.