Evolutionary Computation and Multicriteria Optimization
Evolution is the way how nature solves optimization problem extremly successful. The mechanisms of natural evolution (reproduction, mutation, recombination, and selection) can also be replicated mathematically and are hereby available for any kind of engineering problem for improvement of functional design.
One of the advantages of evolutionary strategies are that they have a very simple core and that they are generally applicable for any kind of problems. They are extremely robust and nearly always find contructive improvements (as long as these are possible at all). Contrary to classical optimization methods one does not need to invest a lot of energy in finding the proper algorthm and settings and can concentrate on the actual engineering part of the problem.
A special expertise of ANDATA is the according mathematical formulation of multicriteria and multidisciplinary optimization targets and fitness functions, which lead to sustainable robust system improvements.
- Artificial Intelligence, Computational Intelligence, SoftComputing, Natural Computation - what's the difference?
- Monte Carlo - isn't that quite expensive?
- Optimization does not work, what can be done?
- Despite having excellent optimization results the real system fails totally – what went wrong?
- Why is such a fuss made about the “requirements”?
- What does "robustness management" mean?
- What does "complexity management" mean?