This paper proposes approaches for adapting chromosome-based evolutionary methods to the Permutation Flow Shop Problem. Two types of individual representation (i.e. encoding methods) are proposed, which are applied on three different chromosome based evolutionary techniques, namely the Genetic Algorithm, the Bacterial Evolutionary Algorithm and the Particle Swarm Optimization method. Both representations are applied on the two former methods, whereas one of them is used for the latter optimization technique. Each mentioned algorithm is involved without and with local search steps as one of its evolutionary operators. Since the evolutionary operators of each technique are established according to the applied representation, this paper deals with a total number of ten different chromosome-based evolutionary methods. The obtained techniques are evaluated via simulation runs carried out on the well-known Taillard's benchmark problem set. Based on the experimental results the approaches for adapting chromosome based evolutionary methods are compared to each other.