### Abstract

Evolutionary graph theory (EGT), studies the ability of a mutant gene to overtake a finite structured population. In this chapter, we describe the original framework for EGT and the major work that has followed it. Here, we will study the calculation of the “fixation probability”—the probability of a mutant taking over a population and focuses on game-theoretic applications. We look at varying topics such as alternate evolutionary dynamics, time to fixation, special topological cases, and game theoretic results.

Original language | English (US) |
---|---|

Title of host publication | SpringerBriefs in Computer Science |

Publisher | Springer |

Pages | 75-91 |

Number of pages | 17 |

Edition | 9783319231044 |

DOIs | |

State | Published - Jan 1 2015 |

### Publication series

Name | SpringerBriefs in Computer Science |
---|---|

Number | 9783319231044 |

ISSN (Print) | 2191-5768 |

ISSN (Electronic) | 2191-5776 |

### Fingerprint

### Keywords

- Evolutionary stability
- Large graph
- Payoff matrix
- Regular graph
- Undirected graph

### ASJC Scopus subject areas

- Computer Science(all)

### Cite this

*SpringerBriefs in Computer Science*(9783319231044 ed., pp. 75-91). (SpringerBriefs in Computer Science; No. 9783319231044). Springer. https://doi.org/10.1007/978-3-319-23105-1_6

**Evolutionary graph theory.** / Shakarian, Paulo; Bhatnagar, Abhinav; Aleali, Ashkan; Shaabani, Elham; Guo, Ruocheng.

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

*SpringerBriefs in Computer Science.*9783319231044 edn, SpringerBriefs in Computer Science, no. 9783319231044, Springer, pp. 75-91. https://doi.org/10.1007/978-3-319-23105-1_6

}

TY - CHAP

T1 - Evolutionary graph theory

AU - Shakarian, Paulo

AU - Bhatnagar, Abhinav

AU - Aleali, Ashkan

AU - Shaabani, Elham

AU - Guo, Ruocheng

PY - 2015/1/1

Y1 - 2015/1/1

N2 - Evolutionary graph theory (EGT), studies the ability of a mutant gene to overtake a finite structured population. In this chapter, we describe the original framework for EGT and the major work that has followed it. Here, we will study the calculation of the “fixation probability”—the probability of a mutant taking over a population and focuses on game-theoretic applications. We look at varying topics such as alternate evolutionary dynamics, time to fixation, special topological cases, and game theoretic results.

AB - Evolutionary graph theory (EGT), studies the ability of a mutant gene to overtake a finite structured population. In this chapter, we describe the original framework for EGT and the major work that has followed it. Here, we will study the calculation of the “fixation probability”—the probability of a mutant taking over a population and focuses on game-theoretic applications. We look at varying topics such as alternate evolutionary dynamics, time to fixation, special topological cases, and game theoretic results.

KW - Evolutionary stability

KW - Large graph

KW - Payoff matrix

KW - Regular graph

KW - Undirected graph

UR - http://www.scopus.com/inward/record.url?scp=85044943594&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85044943594&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-23105-1_6

DO - 10.1007/978-3-319-23105-1_6

M3 - Chapter

T3 - SpringerBriefs in Computer Science

SP - 75

EP - 91

BT - SpringerBriefs in Computer Science

PB - Springer

ER -