A Parallel Genetic Algorithm for Configuring Defect Detection Methods

The detection of defects in steel strips is a very important task which can improve the performance of factories by giving the possibility of early and real-time detection of defects. Defect detection methods have such a large amount of parameters that makes finding the best configuration a complex task. The search space of the value of these parameters is pretty large also, so it is necessary to use a search algorithm in order to reduce the computing time. In this article a genetic algorithm is developed for solving this search problem. The genetic algorithm looks for an optimal or sub-optimal solution without examining the whole search space. In addition, the computing time can be reduced by running the algorithm on a grid of computers. The genetic algorithm designed allows a near-optimal configuration of defect detection methods in a short time.