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 |
|---|---|
| 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 |
|---|---|
| Country | Canada |
| City | Vancouver, BC |
| Period | 16/07/06 → 21/07/06 |
Cite this
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Cooperation, solution concepts and long-term dynamics in the iterated prisoner's dilemma. / Garcia, Julian; Hernandez, German; Galeano, Juan Carlos.
2006 IEEE Congress on Evolutionary Computation, CEC 2006. 2006. p. 1618-1623 1688502.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 -