Using Genetic Algorithms for Exploring the Solution Space in the Case of Automated Product Design

We present a novel approach for automatically create industrial products, namely powertrains consisting of engine, transmission and power shaft. We apply a genetic algorithm for exploring the solution space, consisting of 3000 variants, using various criteria, such as power, efficiency and rotation speed. We compare our results with the ones obtained by a human expert in terms of number of feasible solutions, respectively in terms of best and average price. We prove that the proposed approach is generally better than a human expert in exploring the solution space.