defining implicit objective functions for design problems
In many design tasks it is difficult to explicitly define an objective function. This paper uses machine learning to derive an objective in a feature space based on selected examples of previous designs, thus implicitly capturing the features that distinguish that set from others without requiring a predetermined measure of fitness. A genetic algorithm is used to generate new designs, and these are shown to recognisably display the appropriate features. It is demonstrated that the range of relevant features and optimal solutions is easily varied in proportion to the examples selected to define the objective. Methods for improving the function for GA search are discussed.
machine learning genetic algorithm design/synthesis architecture fitness evaluation