Feature Extraction from Optimization Data via DataModeler's Ensemble Symbolic Regression

We demonstrate a means of knowledge discovery through feature extraction that exploits the search history of an optimization run. We regress a symbolic model ensemble from optimization run search points and their objective scores. The frequency of a variable in the models of the ensemble indicates to what the extent it is an influential feature. Our demonstration uses a genetic programming symbolic regression software package that is designed to be ”off-the-shelf”. By default, the only parameter needed in order to evolve a suite of models is how long the user is willing to wait. Then the user can easily specify which models should go forward in terms of sufficient accuracy and complexity. For illustration purposes, we consider a common design heuristic in serial sensor sequencing: “place the most reliable sensor last”. The heuristic is derived based on the mathematical form of the objective function that lays emphasis on the decision variable pertaining to the last sensor. Fe...

• Type: Conference
• Year: 2010
• Where: LION
• Authors: Kalyan Veeramachaneni, Katya Vladislavleva, Una-May O'Reilly
• Keywords: Feature Extraction, LION 2010, Optimization, Optimization Run, Sensor