The typical conic shape of attraction basins in multi-modal search spaces means that the linear scale of fitness evaluations is mapped against an exponential volume of actual solutions in comparison -- the range of the top 25% in possible fitness for solutions likely translates to less than 1% of the actual volume of the attraction basin.  The effect of this feature will be studied using Monte Carlo simulations on benchmark problems (in one to thirty dimensions) and through the application of benchmark metaheuristics (mostly likely Particle Swarm Optimization) to these problems.