This blog is about Learning and Intelligent OptimizatioN, a combination of modeling, machine learning and optimization.
Why do you need that?
Consider a simple toy example: you want to identify your optimal partner (well ...not so "toy" after all). Your two reasonable objectives to maximize are beauty and intelligence. The problem is not trivial because objectives are mutually incompatible, impossible to find a partner with both maximum intelligence and maximum beauty! Now: imagine that a software vendor wants to sell you his product. According to his words :"You just need to specify your weights for beauty and intelligence and then my software will work and identify the best girlfriend or boyfriend for you". Do you buy this software? Will you be able to give weights and then be happy with the partner that the software will identify for you? Think for a moment (without asking your current partner of course). No? What is the problem? Specifying details about how to combine objectives without looking at some concrete results is puzzling, hard, complicated, and may lead to disappointing results.
Most probably you would like to proceed in the following way:
- Give some preliminary values (maybe just by guessing)
- Examine preliminary results
- Revise your expectations and modify the problem definition
Effective design/problemsolving/optimization is an iterative process with a lot of learning involved! Stay tuned!