Transferring the Concept of Selective Pressure from Evolutionary Strategies to Genetic Algorithms

Most problems of combinatorial optimization like routing, task allocation, or scheduling belong to the class of NP-complete problems and can be solved efficiently only by heuristics. Both, genetic algorithms and evolutionary strategies have a number of drawbacks that reduce their applicability to that kind of problems. In order to overcome some of these problems, this paper looks upon the standard genetic algorithm as an artificial self organizing process. With the purpose of providing concepts that make the algorithm more open for scalability on the one hand, and that fight premature convergence on the other hand, this paper presents an extension of the standard genetic algorithm that doesn't introduce any problem specific knowledge. On the basis of an evolutionary strategy like selective pressure some further improvements like the introduction of a concept to handle multiple crossover operators in parallel or the introduction of a concept of segregation and reunification of smaller subpopulations during the evolutionary process are considered. The additional aspects introduced within the scope of that variants of genetic algorithms are inspired from optimization as well as from the views of bionics. In contrast to contributions 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. In the present paper the new algorithm and some of its variants are discussed for the travelling salesman problem (TSP) as a well documented instance of a multimodal combinatorial optimization problem.