Specifying Critical Inputs in a Genetic Algorithm‐driven Decision Support System: An Automated Facility

We present a simple scheme for the automated, iterative specification of the genetic mutation, crossover, and reproduction (usage) probabilities during run time for a specific genetic algorithm‐driven tool. The tool is intended for supporting static scheduling decisions in flexible manufacturing systems. Using a randomly generated (base) test problem instance, we first assess the method by using it to determine the appropriate levels for specific types of mutation and crossover operators. The level for the third operator, reproduction, may then be inferred. We next report on its ability to choose one or more appropriate crossovers from a set of many such operators. Finally, we compare the method's performance with that of approaches suggested in prior research for the base problem and a number of other test problems. Our experimental findings within the specific scheduling domain studied suggest that the simple method could potentially be a valuable addition to any genetic algorithmbased decision support tool. It is, therefore, worthy of additional investigations.