  Schemata theory, Markov chains, and statistical mechanics have been used to
explain how evolutionary algorithms (EAs) work. Incremental success has been
achieved with all of these methods, but each has been stymied by limitations
related to its less-than-global view. We show that moving the investigation
into topological space improves our understanding of why EAs work.
