Absolute Percent Error Based Fitness Functions for Evolving Forecast Models

Andy Novobilski, University of Tennessee at Chattanooga, USA; Farhad A. Kamangar, University of Texas at Arlington, USA

One aspect of evolutionary computing as a method of data mining, is its intrinsic ability to drive model selection according to a mixed set of criteria. Based on natural selection, evolutionary computing utilizes evaluation of candidate solutions according to a fitness criteria that might or might not share the exact same implementation as the metric used to measure the performance of the selected solution. This paper presents the results of using four different fitness functions to evolve nai've Bayesian networks based on a combination of Mean Absolute Percent Error and Worst Absolute Percent Error values tbr individual population members. In addition to the error measurements tiom both the training and lbrecast evaluations, data is presented that shows APE lbr individual members during the generation and evaluation phase.

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