Evolutionary Algorithms Outperform Gradient Descent on Rugged and Flat Landscapes

Testing across three loss landscapes shows that while gradient descent is faster on smooth slopes (108 steps), evolutionary algorithms using truncation selection and Gaussian mutation successfully navigate rugged dips and zero-slope plateaus where gradient descent fails. Evolution reached the bottom of a flat plateau in 840 evaluations.