This article uses the term "evolutionary algorithm" far too narrowly and it is causing a lot of confusion in the comments. I would STRONGLY recommend checking out the book "Evolutionary Optimization Algorithms" by Dan Simon[1]. It is incredibly readable. The type being referred to in the article is the classic "Genetic Algorithm" variant of evoluationary algorithms. But genetic programming, evolutionary programming, simulated annealing, ant colony optimization, particle swarm optimization, differential evolution, estimation of distribution algorithms, biogeography-based optimizations, cultural algorithms, and opposition-based learning algorithms are just a few examples of other types of evolutionary algorithms.<p>In general, they are a great approach to solving any non-convex optimization problem where gradient descent is not a practical choice.<p>[1]: <a href="https://books.google.com/books/about/Evolutionary_Optimization_Algorithms.html?id=gwUwIEPqk30C" rel="nofollow">https://books.google.com/books/about/Evolutionary_Optimizati...</a>
I have a hard time with the analogy due to how important population dynamics and solution diversity are to evolutionary algorithms.<p>In an EA, each candidate in the population represents a complete potential solution. As the diversity & size of the population increases, the potential for convergence on high quality solutions also increases. I do not see the same concept in diffusion models.