One goal of the Artificial Life field is to achieve a computational system with a complex richness similar to that of biological life. In lieu of the knowledge to achieve this, Open-ended evolution is often cited as a promising method. However, this is also not straightforward because it is unknown how to achieve open-ended evolution in a computational setting. One popular hypothesis is that a continuously changing fitness landscape can drive open-ended evolution toward the evolution of complex organisms. Here, we test this idea using the neuroevolution of neural network foraging agents in a smoothly and continuously changing environment for 500, 000 generations compared to an unchanging static environment. Surprisingly, we find evidence that the degree to which novel solutions are found is very similar between static and dynamic environments.