Jason Zutty
ABSTRACT
Traditional genetic programming only supports the use of arithmetic and logical operators on scalar features. The GTMOEP (Georgia Tech Multiple Objective Evolutionary Programming) framework builds upon this by also handling feature vectors, allowing the use of signal processing and machine learning functions as primitives, in addition to the more conventional operators. GTMOEP is a novel method for automated, data-driven algorithm creation, capable of outperforming human derived solutions.
As an example, GTMOEP was applied to the problem of predicting how long an emergency responder can remain in a hazmat suit before the effects of heat stress cause the user to become unsafe. An existing third-party physics model was leveraged for predicting core temperature from various situational parameters. However, a sustained high heart rate also means that a user is unsafe. To improve performance, GTMOEP was evaluated to predict an expected pull time, computed from both thresholds during human trials.
GTMOEP produced dominant solutions in multiple objective space to the performance of predictions made by the physics model alone, resulting in a safer algorithm for emergency responders to determine operating times in harsh environments. The program generated by GTMOEP will be deployed to a mobile application for their use.
- H. Al-Sahaf, A. Song, and M. Zhang. Hybridisation of genetic programming and nearest neighbour for classification. In 2013 IEEE Congress on Evolutionary Computation (CEC), pages 2650--2657. IEEE, June 2013.Google Scholar
Cross Ref
- K. Bache and M. Lichman. University of california, irvine machine learning repository, 2013.Google Scholar
- K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2):182--197, 2002. Google ScholarDigital Library
- F. Fortin, D. Rainville, M. G. Gardner, M. Parizeau, C. Gagné, et al. DEAP: Evolutionary algorithms made easy. Journal of Machine Learning Research, 13:2171--2175, July 2012. Google ScholarDigital Library
- H. Guo, L. B. Jack, and A. K. Nandi. Feature generation using genetic programming with application to fault classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 35(1):89--99, February 2005. Google ScholarDigital Library
- L. Guo, D. Rivero, J. Dorado, C. R. Munteanu, and A. Pazos. Automatic feature extraction using genetic programming: An application to epileptic eeg classification. Expert Systems with Applications, 38(8):10,425--10,436, August 2011. Google ScholarDigital Library
- Y. Hold-Geoffroy, O. Gagnon, and M. Parizeau. Once you scoop, no need to fork. In Proceedings of the 2014 Annual Conference on Extreme Science and Engineering Discovery Environment, page 60. ACM, 2014. Google ScholarDigital Library
- K. L. Holladay and K. A. Robbins. Evolution of signal processing algorithms using vector based genetic programming. In 15th International Conference on Digital Signal Processing, pages 503 -- 506. IEEE, 2007.Google ScholarCross Ref
- E. Jones, T. Oliphant, P. Peterson, et al. SciPy: Open source scientific tools for Python, 2001--. {Online; accessed 2015-01--26}.Google Scholar
- Y. Lee and L. Tong. Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming. Knowledge-Based Systems, 24(1):66 -- 72, February 2011. Google ScholarDigital Library
- A. J. K. Oudenhuijzen and J. D. J. Layden. From human operator to clothing parameters? 2011.Google Scholar
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825 -- 2830, 2011. Google ScholarDigital Library
- P. Ravisankar, V. Ravi, and I. Bose. Failure prediction of dotcom companies using neural network-genetic programming hybrids. Information Sciences, 180(8):1257 -- 1267, April 2010. Google ScholarDigital Library
- G. Rohling. Multiple objective evolutionary algorithms for independent, computationally expensive objective evaluations. PhD thesis, Georgia Institute of Technology, November 2004. Google ScholarDigital Library
- J. R. Sherrah, R. E. Bogner, and A. Bouzerdoum. The evolutionary pre-processor: Automatic feature extraction for supervised classification using genetic programming. Genetic Programming, pages 304 -- 312, 1997.Google Scholar
- J. Streater. Genetic programming for the automatic construction of features in skin-lesion image classification. Master's thesis, University of Edinburgh, 2010.Google Scholar
- C. D. Vera, E. Alfaro-cid, K. Sharman, and A. I. Esparcia-Alcázar. Genetic programming and serial processing for time series classification. Evolutionary Computation, 22(2):265 -- 285, 2014. Google ScholarDigital Library
- D. Zhang, M. Hifi, Q. Chen, and W. Ye. A hybrid credit scoring model based on genetic programming and support vector machines. volume 7, pages 8 -- 12. IEEE, October 2008. Google ScholarDigital Library
- E. Zitzler, M. Laumanns, and L. Thiele. Spea2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. In Evolutionary Methods for Design, Optimisation and Control with Application to Industrial Problems (EUROGEN 2001), pages 95 -- 100. International Center for Numerical Methods in Engineering (CIMNE), 2002.Google Scholar
Index Terms
- Multiple Objective Vector-Based Genetic Programming Using Human-Derived Primitives
Recommendations
Multiple regression genetic programming
GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary ComputationWe propose a new means of executing a genetic program which improves its output quality. Our approach, called Multiple Regression Genetic Programming (MRGP) decouples and linearly combines a program's subexpressions via multiple regression on the target ...
A Comparison of three evolutionary strategies for multiobjective genetic programming
We report what we believe to be the first comparative study of multi-objective genetic programming (GP) algorithms on benchmark symbolic regression and machine learning problems. We compare the Strength Pareto Evolutionary Algorithm (SPEA2), the Non-...
Solving real-valued optimisation problems using cartesian genetic programming
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computationClassical Evolutionary Programming (CEP) and Fast Evolutionary Programming (FEP) have been applied to real-valued function optimisation. Both of these techniques directly evolve the real-values that are the arguments of the real-valued function. In this ...
Comments