Optimality and stability of symmetric evolutionary games with applications in genetic selection

Yuanyuan Huang; Yiping Hao; Min Wang; Wen Zhou; Zhijun Wu

doi:10.3934/mbe.2015.12.503

Mathematical Biosciences and Engineering, 2015, 12(3): 503-523. doi: 10.3934/mbe.2015.12.503. Primary: 49K30, 65K10, 91A22, 92D15, 92D25.

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Optimality and stability of symmetric evolutionary games with applications in genetic selection

Yuanyuan Huang, Yiping Hao, Min Wang, Wen Zhou, Zhijun Wu

1. Department of Mathematics, Iowa State University, Ames, IA 50011
2. Department of Statistics, Iowa State University, Ames, IA 50011

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Symmetric evolutionary games, i.e., evolutionary games with symmetric fitness matrices, have important applications in population genetics, where they can be used to model for example the selection and evolution of the genotypes of a given population. In this paper, we review the theory for obtaining optimal and stable strategies for symmetric evolutionary games, and provide some new proofs and computational methods. In particular, we review the relationship between the symmetric evolutionary game and the generalized knapsack problem, and discuss the first and second order necessary and sufficient conditions that can be derived from this relationship for testing the optimality and stability of the strategies. Some of the conditions are given in different forms from those in previous work and can be verified more efficiently. We also derive more efficient computational methods for the evaluation of the conditions than conventional approaches. We demonstrate how these conditions can be applied to justifying the strategies and their stabilities for a special class of genetic selection games including some in the study of genetic disorders.

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Keywords: evolutionary games; genetic selection; population genetics; evolutionary stability.; Evolutionary biology; generalized knapsack problems

Citation: Yuanyuan Huang, Yiping Hao, Min Wang, Wen Zhou, Zhijun Wu. Optimality and stability of symmetric evolutionary games with applications in genetic selection. Mathematical Biosciences and Engineering, 2015, 12(3): 503-523. doi: 10.3934/mbe.2015.12.503

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Copyright Info: 2015, Yuanyuan Huang, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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