SASEGASA: An Evolutionary Algorithm for Retarding Premature Convergence by Self-adaptive Selection Pressure Steering

This paper presents a new generic Evolutionary Algorithm (EA) for retarding the unwanted effects of premature convergence. This is accomplished by a combination of interacting methods. To be intent on this a new selection scheme is introduced, which is designed to maintain the genetic diversity within the population by advantageous self-adaptive steering of selection pressure. Additionally this new selection model enables a quite intuitive condition to detect premature convergence. Based upon this newly postulated basic principle the new selection mechanism is combined with the already proposed Segregative Genetic Algorithm (SEGA) [3], an advanced Genetic Algorithm (GA) that introduces parallelism mainly to improve global solution quality. As a whole, a new generic evolutionary algorithm (SASEGASA) is introduced. The performance of the algorithm is evaluated on a set of characteristic benchmark problems. Computational results show that the new method is capable of producing highest quality solutions without any problem-specific additions.