### Abstract

Solution concepts help designing co-evolutionary algorithms by interfacing search mechanisms and problems. This work analyses co-evolutionary dynamics by coupling the notion of solution concept with a Markov chain model of coevolution. It is shown that once stationarity has been reached by the Markov chain, and given a particular solution concept of interest, the dynamics can be seen as a Bernoulli process describing how the algorithm visits solution and non-solution sets. A particular analysis is presented using the Iterated Prisoner's Dilemma. By numerically computing the Markov chain transition matrices and stationary distributions, a complex and strong relation between variation and selection is observed.

Original language | English |
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Title of host publication | 2006 IEEE Congress on Evolutionary Computation, CEC 2006 |

Pages | 1618-1623 |

Number of pages | 6 |

Publication status | Published - 1 Dec 2006 |

Externally published | Yes |

Event | 2006 IEEE Congress on Evolutionary Computation, CEC 2006 - Vancouver, BC, Canada Duration: 16 Jul 2006 → 21 Jul 2006 |

### Conference

Conference | 2006 IEEE Congress on Evolutionary Computation, CEC 2006 |
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Country | Canada |

City | Vancouver, BC |

Period | 16/07/06 → 21/07/06 |

### Cite this

*2006 IEEE Congress on Evolutionary Computation, CEC 2006*(pp. 1618-1623). [1688502]

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*2006 IEEE Congress on Evolutionary Computation, CEC 2006.*, 1688502, pp. 1618-1623, 2006 IEEE Congress on Evolutionary Computation, CEC 2006, Vancouver, BC, Canada, 16/07/06.

**Cooperation, solution concepts and long-term dynamics in the iterated prisoner's dilemma.** / Garcia, Julian; Hernandez, German; Galeano, Juan Carlos.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Cooperation, solution concepts and long-term dynamics in the iterated prisoner's dilemma

AU - Garcia, Julian

AU - Hernandez, German

AU - Galeano, Juan Carlos

PY - 2006/12/1

Y1 - 2006/12/1

N2 - Solution concepts help designing co-evolutionary algorithms by interfacing search mechanisms and problems. This work analyses co-evolutionary dynamics by coupling the notion of solution concept with a Markov chain model of coevolution. It is shown that once stationarity has been reached by the Markov chain, and given a particular solution concept of interest, the dynamics can be seen as a Bernoulli process describing how the algorithm visits solution and non-solution sets. A particular analysis is presented using the Iterated Prisoner's Dilemma. By numerically computing the Markov chain transition matrices and stationary distributions, a complex and strong relation between variation and selection is observed.

AB - Solution concepts help designing co-evolutionary algorithms by interfacing search mechanisms and problems. This work analyses co-evolutionary dynamics by coupling the notion of solution concept with a Markov chain model of coevolution. It is shown that once stationarity has been reached by the Markov chain, and given a particular solution concept of interest, the dynamics can be seen as a Bernoulli process describing how the algorithm visits solution and non-solution sets. A particular analysis is presented using the Iterated Prisoner's Dilemma. By numerically computing the Markov chain transition matrices and stationary distributions, a complex and strong relation between variation and selection is observed.

UR - http://www.scopus.com/inward/record.url?scp=34547333375&partnerID=8YFLogxK

M3 - Conference Paper

SN - 0780394879

SN - 9780780394872

SP - 1618

EP - 1623

BT - 2006 IEEE Congress on Evolutionary Computation, CEC 2006

ER -