Evolution strategies (ESes) are even easier than GAs as they are primarily based on mutation to create a new population.

Mutation is performed by adding values that have been sampled from a normal distribution to the genotype. A very simple version of ES is obtained by just selecting the most performant individual across the whole population and sampling the next generation from a normal distribution with a fixed standard deviation and a mean equal to that of the best-performing individual.

Outside of the sphere of small problems, using this algorithm is not recommended. This is because following only a single leader and using a fixed standard deviation could prevent potential solutions from exploring a more diverse search space. As a consequence, the solution to this method would probably end in a narrow local minimum. An immediate and better strategy would be to generate the offspring by combining the  top performing candidate solutions and weighing them by their fitness rank. Ranking the individuals according to their fitness values is called fitness ranking. This strategy is preferred to using the actual fitness values as it is invariant to the transformation of the objective function and it prevents the new generation from moving too much toward a possible outlier.