Stepping stones and hidden haystacks: when a genetic algorithm defeats a hillclimber

Following intuitive notions on gross aspects of how a GA behaves, we are able to demonstrate how to construct functions on which a GA will greatly outperform a hillclimber. This augments related work on long path problems, and gene switch cost functions, which describe similarly `GA appropriate´ landscapes but on rather less intuitively clear grounds. Although artificial, the construction of these problems relies on certain gross landscape features that may be a priori estimated in the case of many real problems, incrementing the collection of descriptive tools with which to assess potential amenability to evolutionary search. We argue in particular that a specific notion of hillclimbing behaviour can with certain merits, and with certain qualifications, be included in this collection