Genetic Programming Based Choquet Integral for Multi-Source Fusion


While the Choquet integral (ChI) is a powerful parametric nonlinear aggregation function, it has limited scope and is not a universal function generator. Herein, we focus on a class of problems that are outside the scope of a single ChI. Namely, we are interested in tasks where different subsets of inputs require different ChIs. Herein, a genetic program (GP) is used to extend the ChI, referred to as GpChI hereafter, specifically in terms of compositions of ChIs and/or arithmetic combinations of ChIs. An algorithm is put forth to learn the different GP ChIs via genetic algorithm (GA) optimization. Synthetic experiments demonstrate GpChI in a controlled fashion, i.e., we know the answer and can compare what is learned to the truth. Real-world experiments are also provided for the multi-sensor fusion of electromagnetic induction (EMI) and ground penetrating radar (GPR) for explosive hazard detection. Our multi-sensor fusion experiments show that there is utility in changing aggregation strategy per different subsets of inputs (sensors or algorithms) and fusing those results.




R. Smith, D. Anderson, A. Zare, J. Ball, B. Alvey, J. Fairley, and S. Howington, "Genetic Programming Based Choquet Integral for Multi-Source Fusion," in IEEE Int. Conf. Fuzzy Systems (FUZZ-IEEE), 2017.
Title = {Genetic Programming Based Choquet Integral for Multi-Source Fusion},
Author = {Ryan E. Smith and Derek T. Anderson and Alina Zare and John E. Ball and Brendan Alvey and Josh R Fairley and Stacy E Howington},
Booktitle = {IEEE Int. Conf. Fuzzy Systems (FUZZ-IEEE)},
Year = {2017},
Month = {Jul.}