Adaptive Genetic Algorithm for Optimal Printed Circuit Board Assembly Planning

Abstract We present a novel application of the genetic algorithm approach for solving the problem of planning optimal placement/insertion sequence and machine setup in primed circuit board (PCB) assembly. The algorithm starts with feasible solutions and utilizes genetic operators to iteratively generate potentially better solutions in the optimization process, similar to the biological evolution process. We first describe the basic algorithm and its application to optimal planning for some popular PCB assembly machines. We then describe an adaptive genetic algorithm, which has its rates of genetic operators changed automatically during the iterative optimization process. We use a Wilcoxon signed rank test to show its performance improvement over the fixed-rate genetic algorithm