Measuring the Searched Space to Guide Efficiency: The Principle and Evidence on Constraint Satisfaction

In this paper we present a new tool to measure the efficiency of evolutionary algorithms by storing the whole searched space of a run, a process whereby we gain insight into the number of distinct points in the state space an algorithm has visited as opposed to the number of function evaluations done within the run. This investigation demonstrates a certain inefficiency of the classical mutation operator with mutation-rate 1/l, where l is the dimension of the state space. Furthermore we present a model for predicting this inefficiency and verify it empirically using the new tool on binary constraint satisfaction problems.