Fitness function to find Game Equilibria using Genetic Algorithms

Mahathi Gunturu; Giridhar N. Shakarad; Sanjay Singh

doi:10.1109/ICACCI.2017.8126058

Fitness function to find Game Equilibria using Genetic Algorithms

Mahathi Gunturu, Giridhar N. Shakarad, Sanjay Singh

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

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 language	English
Title of host publication	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1531-1534
Number of pages	4
Volume	2017-January
ISBN (Electronic)	9781509063673
DOIs	https://doi.org/10.1109/ICACCI.2017.8126058
Publication status	Published - 30-11-2017
Event	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 - Manipal, Mangalore, India Duration: 13-09-2017 → 16-09-2017

Conference

Conference	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
Country/Territory	India
City	Manipal, Mangalore
Period	13-09-17 → 16-09-17

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Science Applications
Information Systems

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10.1109/ICACCI.2017.8126058

Cite this

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title = "Fitness function to find Game Equilibria using Genetic Algorithms",

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.",

author = "Mahathi Gunturu and Shakarad, {Giridhar N.} and Sanjay Singh",

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publisher = "Institute of Electrical and Electronics Engineers Inc.",

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note = "2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 ; Conference date: 13-09-2017 Through 16-09-2017",

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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 proceeding › Conference contribution

TY - GEN

T1 - Fitness function to find Game Equilibria using Genetic Algorithms

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AU - Shakarad, Giridhar N.

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N2 - 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.

AB - 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.

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ER -

Fitness function to find Game Equilibria using Genetic Algorithms

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