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The use of genetic algorithms for optimizing age structure in breeding populations when inbreeding depresses genetic gain through effects on reproduction

Published online by Cambridge University Press:  18 August 2016

S. A. Meszaros ,
R. G. Banks ,
J. H. J. van der Werf  and
M. Goddard
S. A. Meszaros
Affiliation:
Animal Science, University of New England, Armidale, NSW 2351 Australia
R. G. Banks
Affiliation:
LAMBPLAN, Animal Science, University of New England, Armidale, NSW 2351 Australia
J. H. J. van der Werf
Affiliation:
Animal Science, University of New England, Armidale, NSW 2351 Australia
M. Goddard*
Affiliation:
Animal Genetics Breeding Unit, University of New England, Armidale, NSW 2351 Australia
*
† Present address: Institute of Food and Land Resources, University of Melbourne, Parkville, Victoria 3052, Australia.
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Abstract

A genetic algorithm (GA) was used to find optimal male and female age distributions in a natural mating system that maximizes cumulative response to mass selection over a 20-year time horizon for the case where inbreeding affects reproduction at 0·0 (F-0) and 0·1 (F-10) per 0·1 inbreeding coefficient. Twenty breeding female population sizes were considered ranging from 25 to 500 breeding females distributed across five age groups. Loss of response due to inbreeding effects on reproduction ranged from 19.4% and 15.5% in small breeding female populations to 2.5% and 5.2% in large breeding female populations when number of males was fixed (FX) or optimized (OP), respectively. OP resulted in an increase in response over FX ranging from 0·0 % to 69.3% for F-0 and 0·0 % to 77.6% for F-10. The potential loss of genetic gain that resulted from ignoring the inbreeding effects upon reproduction when they really existed ranged from 0·1 % to 44.6%. The potential loss of genetic gain that resulted from including inbreeding effects upon reproduction when they did not exist ranged from 0·1% to 3.9%. Optimal male and female age structures depended upon breeding female population size, the number of breeding males and inbreeding effects. Ignoring inbreeding effects upon reproduction may result in over estimation of response to selection. Use of a GA allowed accounting for complex relationships in the optimization.

Keywords

algorithmsgenetic gaininbreeding depressionoptimization
Type
Research Article
Information
Animal Science , Volume 68 , Issue 3 , April 1999 , pp. 457 - 499
Copyright
Copyright © British Society of Animal Science 1999

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References

Back, T. 1992. Self-adaptation in genetic algorithms. Proceedings of the first European conference on artificial life, vol. 1, pp. 263–271.Google Scholar
Back, T. and Schwefel, H. P. 1993. An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation 1: 1–23.Google Scholar
Brash, L. D., Fogarty, N. M. and Gilmour, A. R. 1994. Reproductive performance and genetic parameters for Australian Dorset sheep. Australian Journal of Agricultural Research 45: 427–441.CrossRefGoogle Scholar
Brisbane, J. R. and Gibson, J. P. 1994. Balancing selection response and rate of inbreeding by including genetic relationships in selection decisions. Proceedings of the fifth world congress on genetics applied to livestock production, Guelph, vol. 19, pp. 135–138.Google Scholar
Buhner, M. G. 1971. The effect of selection on genetic variability. American Naturalist 105: 201–211.Google Scholar
Burrows, P. N. 1972. Expected selection differentials for directional selection. Biometrics 28: 1091–1100.CrossRefGoogle ScholarPubMed
Cunha, G. A., Oliveira, P. and Covas, J. A. 1997. Use of genetic algorithms in multicriteria optimization to solve industrial problems. Proceedings of the seventh international conference on genetic algorithms, pp. 682–688.Google Scholar
Falconer, D. S. 1989. Introduction to quantitative genetics, third edition. Longman, Essex.Google Scholar
Goldberg, D. E. 1989. Genetic algorithms in search, optimization and machine learning. Addison-Wesley Reading, Massachusetts.Google Scholar
Grundy, B. and Hill, W. G. 1993. A method for reducing inbreeding with best linear unbiased prediction. Animal Production 56: 427 (abstr.).Google Scholar
Hayes, B. J., Shepherd, R. K. and Newman, S. 1997. Selecting mating pairs with genetic algorithms. Proceedings of the 12th conference of the Association for the Advancement of Animal Breeding and Genetics, vol. 12, pp. 108–112.Google Scholar
Holland, J. H. 1975. Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor.Google Scholar
Horton, B. 1996. A method of using a genetic algorithm to examine the optimum structure of the Australian sheep breeding industry: open-nucleus breeding systems, МОЕТ and AI. Australian Journal of Experimental Agriculture 36: 249–258.CrossRefGoogle Scholar
Kinghorn, B. P. 1998. Managing genetic change under operational and cost constraints. Thirty-sixth national congress of the South African Association of Animal Science 36: 9–16.Google Scholar
Klieve, H. M., Kinghorn, B. P. and Barwick, S. A. 1994. The joint regulation of genetic gain and inbreeding under mate selection. Journal of Animal Breeding and Genetics 111: 81–88.CrossRefGoogle ScholarPubMed
Lamberson, W. R. and Thomas, D. L. 1984. Effects of inbreeding in sheep: a review. Animal Breeding Abstracts 52: 287–297.Google Scholar
Loughlin, D. H. and Ranjithan, S. 1997. The neighborhood constraint method: a genetic algorithm-based multiobjective optimization technique. Proceedings of the seventh international conference on genetic algorithms, pp. 666–673.Google Scholar
Mahfoud, S. W. 1992. Niching methods for genetic algorithms. Doctoral dissertation, University of Illinois at Mrb ana-Champaign.Google Scholar
Meszaros, S. A., Banks, R. G., Kinghorn, B. P. and Shafto, A. M. 1997. Design considerations in development of breeding strategies in a complex national industry context. Proceedings of the 12th conference of the Association for the Advancement of Animal Breeding and Genetics, vol. 12, pp. 95–98.Google Scholar
Meuwissen, T. H. E. 1997. Maximizing the response of selection with a predefined rate of inbreeding. Journal of Animal Science 75: 934–940.Google Scholar
Quinton, M. and Smith, C. 1995. Comparison of evaluation-selection systems for maximizing genetic response at the same level of inbreeding. Journal of Animal Scienc 70: 2208–2212.Google Scholar
Quinton, M., Smith, C. and Goddard, M. E. 1992. Comparison of selection methods at the same level of inbreeding. Journal of Animal Science 70: 1060–1067.Google Scholar
Rattray, M. and Shapiro, J. L. 1996. Noisy fitness evaluation in genetic algorithms and the dynamics of learning. Proceedings of Foundations of Genetic Algorithms 1: 69–93.Google Scholar
Santiago, E. and Caballero, A. 1995. Effective size of populations under selection. Genetics 139: 1013–1030.Google Scholar
Wray, N. R. and Goddard, M. E. 1994. Increasing long-term response to selection. Genetics, Selection, Evolution 26: 431–451.Google Scholar