I. Introduction
Generic randomized search heuristics proved to be highly successful in solving diverse algorithmic problems. Their strength from the practitioner's point of view is that such algorithms are composed of generic parts (e.g., representations, mutation operators, fitness functions) that can easily be reused. Also, the hope is that an expert in such methods can easily solve algorithmic problems by plugging together suitable generic components without fully analyzing the problem itself. Of course, to this aim suitable representations and mutation operators must be known. A series of papers on the Euler tour problem [3]–[5] demonstrates how more adequate representations yield better algorithms.