This paper looks upon the standard genetic algorithm as an artificial self-organizing process. With the purpose to provide concepts that make the algorithm more open for scalability 011 the one hand, and that fight premature convergence 011 the other hand, this paper presents two extensions of the standard genetic algorithm without introducing any problem specific knowledge, as done in many problem specific heuristics 011 the basis of genetic algorithms. In contrast to con tributions in the field of genetic algorithms that introduce new coding standards and operators for certain problems, the introduced approach should be considered as a heuristic appliable to multiple problems of combinatorial optimization, using exactly the same coding standards and operators for crossover and mutation, as done when treating a certain problem with a standard genetic algorithm. The additional aspects introduced within the scope of segregative genetic algorithms (SEGA) are inspired from optimization as well as from the views of bionics. In the present paper the new algorithm is discussed for the travelling salesman problem (TSP) as a well documented instance of a multimodal combinatorial optimization problem. In contrast to all other evolutionary heuristics that do not use any additional problem specific knowledge, we obtain solutions close to the best known solution for all considered benchmark problems (symmetric as well as asymmetric benchmarks) which represents a new attainment when applying evolutionary computation to the TSP.