Genetic algorithms (GA) have populations where it has an offspring in every generation usually the same quantity than the original population, so, if a child results from two good solutions (parents) but very different and it has a bad fitness, it will be not selected for being part of the next generation (elitism, where only the best n individual survive). But maybe you can find a new and good solution from that combination, and, in that case, it will be part of the new generation.
Maybe, if your problem has multiple solutions, the population can be formed by clusters of solutions that improve between them.
But there are many algorithms in GA (and Evolutionary Algorithms in general), so you need to read the details. Fortunately, there are many frameworks where only you need to define your problem and it can rapidly make comparisons between them.