Fitness function to find Game Equilibria using Genetic Algorithms

Mahathi Gunturu, Giridhar N. Shakarad, Sanjay Singh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In Non cooperative Game Theory, Nash Equilibrium can be computed by finding the best response strategy for each player. However this problem cannot be solved deterministically in polynomial time. For some finite games, there might be more than one pure strategy Game Equilibrium. In such cases, the most optimal set of solutions give the Game Equilibria. Evolutionary Algorithms and specifically Genetic Algorithms, based on Pareto dominance used in multi-objective optimization do not incorporate the Nash dominance and the extent of dominance in finding the equilibria. Many pairs of solutions do not dominate each other based on the generative relation of Pareto dominance and Nash Ascendancy. In this paper a fitness function based on the generative relation of Nash Ascendancy has been proposed to enhance the comparison of two individuals in a population. It assigns a better fitness value to pair of individuals that do not dominate each other.

Original languageEnglish
Title of host publication2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1531-1534
Number of pages4
Volume2017-January
ISBN (Electronic)9781509063673
DOIs
Publication statusPublished - 30-11-2017
Event2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 - Manipal, Mangalore, India
Duration: 13-09-201716-09-2017

Conference

Conference2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
CountryIndia
CityManipal, Mangalore
Period13-09-1716-09-17

Fingerprint

Game theory
Multiobjective optimization
Evolutionary algorithms
Genetic algorithms
Polynomials

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems

Cite this

Gunturu, M., Shakarad, G. N., & Singh, S. (2017). Fitness function to find Game Equilibria using Genetic Algorithms. In 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 (Vol. 2017-January, pp. 1531-1534). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICACCI.2017.8126058
Gunturu, Mahathi ; Shakarad, Giridhar N. ; Singh, Sanjay. / Fitness function to find Game Equilibria using Genetic Algorithms. 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1531-1534
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Gunturu, M, Shakarad, GN & Singh, S 2017, Fitness function to find Game Equilibria using Genetic Algorithms. in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 1531-1534, 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, Manipal, Mangalore, India, 13-09-17. https://doi.org/10.1109/ICACCI.2017.8126058

Fitness function to find Game Equilibria using Genetic Algorithms. / Gunturu, Mahathi; Shakarad, Giridhar N.; Singh, Sanjay.

2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 1531-1534.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Gunturu M, Shakarad GN, Singh S. Fitness function to find Game Equilibria using Genetic Algorithms. In 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. Vol. 2017-January. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1531-1534 https://doi.org/10.1109/ICACCI.2017.8126058