A Comparison of Four Types of Evolution Strategies for Beef Cattle Feed Optimization

Tirana Noor Fatyanosa, Wayan Firdaus Mahmudy, Marjuki Marjuki

Abstract


Beef cattle feed optimization is a multi-objective problem. For different weight of beef cattle, the required nutrition is also different. The feed also requires a balance of nutrients with a low price. This paper presents a comparison of four types of Evolution Strategies (ES) for beef cattle feed optimization. The results of our experiments suggest that the performance and robustness of ES (µ,λ), ES (µ/ρ,λ), and ES (µ+λ) are comparable, while ES (µ/ρ+λ) performs slightly worse. This fact together promotes ES (µ/ρ,λ) as the most robust for practical use. The experimental results show that the feed price obtained from ES (µ/ρ,λ) is 5524.465 with fitness value of 1.809861462.

Keywords


Beef Cattle Feed Optimization; Evolution Strategies;

Full Text:

PDF

References


University of Missouri-Columbia, ‘Introduction to Beef Production’. [Online]. Available: https://dese.mo.gov/sites/default/files/aged-BeefStudent-Ref..pdf. [Accessed: 13-Feb-2017].

Australian Lot Feeders Association, ‘About the Australian Feedlot Industry’, 2015. [Online]. Available: http://feedlots.com.au/industry/feedlot-industry/about/. [Accessed: 15- Feb-2017].

Infonet Biovision, ‘Animal nutrition and feed rations’, 2016. [Online]. Available: http://www.infonet-biovision.org/AnimalHealth/animalnutrition-and-feed-rations. [Accessed: 15-Feb-2017].

M. Nabasirye, J. Y. T. Mugisha, F. Tibayungwa, and C. C. Kyarisiima, ‘Optimization of input in animal production: A linear programming approach to the ration formulation problem’, Int. Res. J. Agric. Sci. Soil Sci., vol. 1, no. 7, pp. 221–226, 2011.

P. Saxena, ‘Optimization techniques for animal diet formulation’, vol. 1, no. 2, pp. 1–5, 2011.

A. A. Altun and M. A. Şahman, ‘Cost optimization of mixed feeds with the particle swarm optimization method’, Neural Comput. Appl., vol. 22, no. 2, pp. 383–390, Feb. 2013.

V. N. Wijayaningrum and W. F. Mahmudy, ‘Fodder Composition Optimization Using Modified Genetic Algorithm’, Int. J. Intell. Eng. Syst., (in press), 2017.

T. N. Fatyanosa and W. F. Mahmudy, ‘Modified Evolution Strategies for Beef Cattle Feed Optimization’, Int. J. Intell. Eng. Syst., (in press), 2017.

T. N. Fatyanosa, F. Utaminingrum, and M. Data, ‘Linear Programming Initialization Method of Evolution Strategies for Beef Cattle Feed Optimization’, J. Telecommun. Electron. Comput. Eng., (in press), 2017.

V. N. Wijayaningrum and W. F. Mahmudy, ‘Optimization of Poultry Feed Composition Using Hybrid Adaptive Genetic Algorithm and Simulated Annealing’, J. Telecommun. Electron. Comput. Eng., (in press), 2017.

I. Vatolkin, W. Theimer, and G. Rudolph, ‘Design and comparison of different evolution strategies for feature selection and consolidation in music classification’, in 2009 IEEE Congress on Evolutionary Computation, 2009, pp. 174–181.

T. J. Mitchell and A. G. Pipe, ‘A Comparison of Evolution StrategyBased Methods for Frequency Modulated Musical Tone Timbre Matching’, in Proceedings of the 7th International Conference on Adaptive Computing in Design and Manufacture, 2006.

J. F. Ramírez and O. Fuentes, ‘Spectral Analysis Using Evolution Strategies’, in IASTED International Conference on Artificial Intelligence and Soft Computing, 2002.

T. Jansen, K. A. DeJong, and I. Wegener, ‘On the choice of the offspring population size in evolutionary algorithms’, Evol. Comput., vol. 13, no. 4, pp. 413–440, 2005.

G. J. LaPorte, J. Branke, and C.-H. Chen, ‘Adaptive Parent Population Sizing in Evolution Strategies’, Evol. Comput., vol. 23, no. 3, pp. 397– 420, Sep. 2015.

National Research Council, ‘Nutrient Requirements of Poultry’, 1994.

R. L. Preston, ‘2016 Feed Composition Tables’, Beef Magazine, Minneapolis, Minnesota, p. 18,21-22,29,34, Mar-2016.

National Research Council, Nutrient Requirements of Beef Cattle, Seventh Re. 2000.

H.-G. Beyer and H.-P. Schwefel, Evolution strategies – A comprehensive introduction, vol. 1, no. 1. Berlin: Kluwer Academic Publisher, 2002.

A. Abraham, N. Nedjah, L. De, and M. Mourelle, ‘Evolutionary Computation: from Genetic Algorithms to Genetic Programming’, Genet. Syst. Program., vol. 20, pp. 1–20, 2006.

N. Hansen, D. V. Arnold, and A. Auger, ‘Evolution Strategies’, in Springer Handbook of Computational Intelligence, Berlin: Springer Berlin Heidelberg, 2015, pp. 871–898.

M. Jaindl, A. Kostinger, C. Magele, and W. Renhart, ‘Multi-Objective Optimization Using Evolution Strategies’, Facta Univ. Ser. Electron. Energ., vol. 22, no. 2, pp. 159–174, 2009.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

ISSN: 2180-1843

eISSN: 2289-8131