A generic evolutionary computation approach based upon genetic algorithms and evolution strategies

Many problems that are treated by genetic algorithms belong to the class of NP-complete problems. The vantage of genetic algorithms when being applied to such kind of problems lies in the ability to search through the solution space in a broader sense than other heuristic methods that are based upon neighborhood search methods. Nevertheless, also genetic algorithms are frequently faced with a problem which, at least in its impact, is quite similar to the problem of stagnating in a local but not global solution what typically occurs when applying neighborhood based searches to hard problems with multimodal solution spaces. This drawback, called premature convergence in the terminology of genetic algorithms, occurs when the population of a genetic algorithm reaches such a suboptimal state that the genetic operators can no longer produce offspring that outperform their parents. During the last decades plenty of work has been investigated to introduce new coding standards and operators in order to overcome this essential handicap of genetic algorithms. As these coding standards and the belonging operators are rather problem specific in general we try to take a different approach and look upon the concepts of genetic algorithms as an artificial self organizing process in a bionically inspired generic way in order to improve the global convergence behaviour of genetic algorithms independently of the actually employed implementation. In doing so we have introduced an advanced selection model for genetic algorithms that allows adaptive selective pressure handling in a way that is quite similar to evolution strategies. This enhanced genetic algorithm-model allows further extensions 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. Both extensions rely on a variable selective pressure because the general conditions may change during the evolutionary process. The experimental part of the paper discusses the new algorithms for the traveling salesman problem (TSP) as a well documented instance of a multimodal combinatorial optimization problem achieving results which significantly outperform the results obtained with a contrastable genetic algorithm.