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 require-ments which led us to an additional procedure that uses specific knowledge upon gene-objective relations to guide cross-over mechanisms.