Peak analysis for characterizing evolutionary behavior

Evolutionary algorithms (EAs) use a simplified abstraction of biological evolution to interact with a fitness landscape over multiple generations. The traditional approach to exploring evolutionary motivated search relies on performance comparisons of competing designs for a common problem domain. This approach has been useful in developing an intuition of when one mechanism is superior to another. However, this intuition has developed in the absence of a clear understanding of how fitness landscape topology impacts the search process in high-dimensional space, due to the lack of high-dimensional visualization tools. Proposed is a behavior measure derived from a known set of all problem domain optima, which is used as system of landmarks. Using these landmarks, evolutionary system progress can be tracked in the problem domain and characterized with an information theoretic metric. Thus, this technique provides an intuition that takes into account the problem domain topology, allowing the behavior of different algorithm configurations to be compared as they interact with a given topology.