Evolutionary algorithms applied to likelihood function maximization during poisson, logistic, and Cox proportional hazards regression analysis

Leif E. Peterson

doi:10.1109/CEC.2014.6900660

Evolutionary algorithms applied to likelihood function maximization during poisson, logistic, and Cox proportional hazards regression analysis

Leif E. Peterson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

Metaheuristics based on genetic algorithms (GA), covariance matrix self-adaptation evolution strategies (CMSA-ES), particle swarm optimization (PSO), and ant colony optimization (ACO) were used for minimizing deviance for Poisson regression and maximizing the log-likelihood function for logistic regression and Cox proportional hazards regression. We observed that, in terms of regression coefficients, CMSA-ES and PSO metaheuristics were able to obtain solutions that were in better agreement with Newton-Raphson (NR) when compared with GA and ACO. The rate of convergence to the NR solution was also faster for CMSA-ES and PSO when compared with ACO and GA. Overall, CMSA-ES was the best-performing method used. Key factors which strongly influence performance are multicollinearity, shape of the log-likelihood gradient, and positive definiteness of the Hessian matrix.

Original language	English (US)
Title of host publication	Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1054-1061
Number of pages	8
ISBN (Electronic)	9781479914883
DOIs	https://doi.org/10.1109/CEC.2014.6900660
State	Published - Sep 16 2014
Event	2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing, China Duration: Jul 6 2014 → Jul 11 2014

Publication series

Name	Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014

Other

Other	2014 IEEE Congress on Evolutionary Computation, CEC 2014
Country/Territory	China
City	Beijing
Period	7/6/14 → 7/11/14

ASJC Scopus subject areas

Artificial Intelligence
Computational Theory and Mathematics
Theoretical Computer Science

Access to Document

10.1109/CEC.2014.6900660

Cite this

Peterson, L. E. (2014). Evolutionary algorithms applied to likelihood function maximization during poisson, logistic, and Cox proportional hazards regression analysis. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014 (pp. 1054-1061). Article 6900660 (Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2014.6900660

Evolutionary algorithms applied to likelihood function maximization during poisson, logistic, and Cox proportional hazards regression analysis. / Peterson, Leif E.
Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1054-1061 6900660 (Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Peterson, LE 2014, Evolutionary algorithms applied to likelihood function maximization during poisson, logistic, and Cox proportional hazards regression analysis. in Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014., 6900660, Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014, Institute of Electrical and Electronics Engineers Inc., pp. 1054-1061, 2014 IEEE Congress on Evolutionary Computation, CEC 2014, Beijing, China, 7/6/14. https://doi.org/10.1109/CEC.2014.6900660

Peterson LE. Evolutionary algorithms applied to likelihood function maximization during poisson, logistic, and Cox proportional hazards regression analysis. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 1054-1061. 6900660. (Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014). doi: 10.1109/CEC.2014.6900660

Peterson, Leif E. / Evolutionary algorithms applied to likelihood function maximization during poisson, logistic, and Cox proportional hazards regression analysis. Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 1054-1061 (Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014).

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abstract = "Metaheuristics based on genetic algorithms (GA), covariance matrix self-adaptation evolution strategies (CMSA-ES), particle swarm optimization (PSO), and ant colony optimization (ACO) were used for minimizing deviance for Poisson regression and maximizing the log-likelihood function for logistic regression and Cox proportional hazards regression. We observed that, in terms of regression coefficients, CMSA-ES and PSO metaheuristics were able to obtain solutions that were in better agreement with Newton-Raphson (NR) when compared with GA and ACO. The rate of convergence to the NR solution was also faster for CMSA-ES and PSO when compared with ACO and GA. Overall, CMSA-ES was the best-performing method used. Key factors which strongly influence performance are multicollinearity, shape of the log-likelihood gradient, and positive definiteness of the Hessian matrix.",

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Evolutionary algorithms applied to likelihood function maximization during poisson, logistic, and Cox proportional hazards regression analysis

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