Genetic engineering versus natural evolution: Genetic algorithms with deterministic operators

Genetic algorithms (GA) have several important features that predestine them to solve design problems. Their main disadvantage however is the excessively long run-time that is needed to deliver satisfactory results for large instances of complex design problems. The main aims of this paper are (1) to demonstrate that the effective and efficient application of the GA concept to design problem solving requires substitution of the basic GAs natural evolution by genetic engineering (GE), (2) to propose and discuss the concept of a genetic engineering algorithm (GEA), and (3) to show how to apply the GEA to solve synthesis problems. In this paper, an effective and efficient GE scheme is proposed and applied to solve an important design problem: the minimal input support problem. In almost all cases, our GEA produces strictly optimal results and realizes a very good trade-off between effectiveness and efficiency. The experimental results clearly demonstrate that the proposed GE scheme is suitable for solving design problems and its application results in very effective and efficient GEAs