Complementary selection and variation for an efficient multiobjective optimization of complex systems

Real-world applications generally distinguish themselves from theoretical developments in that they are much more complex and varied. As a consequence, better models require more details, new methods and, finally, more complexity. By confronting a benchmark evolutionary algorithm with an automotive gearbox with hundreds of parameters to optimize, we were able to observe new requirements which led us to an additional procedure that uses specific knowledge upon gene-objective relations to guide cross-over mechanisms