Abstract
Genetic algorithm (GA) is a new method to solve combinatorial optimization problems by simulating the process of natural evolution. If individuals in GA have constraints, an individual happens to correspond to an infeasible solution, that is, the individual has lethal genes. In this case, GA can not search freely in the solution space. Consequently, the performance of GA may be degraded.
This paper aims at improving the performance for such a GA that many lethal genes are generated in the search process. For this purpose, a modified flowshop schedule problem is considered as a case study. An additional constraint for this problem is that each product has a definite due date to be completed. We show a numerical result for examining the influence of lethal gene on the accuracy of the solution obtained. Furthermore, we propose two procedures for improving GA and compare their effectiveness in the process generating many lethal genes.
