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 | IEEE Congress on Evolutionary Computation 2006 - Vancouver, Canada Duration: 16 Jul 2006 → 21 Jul 2006 https://ieeexplore.ieee.org/xpl/conhome/11108/proceeding (Proceedings) |
Conference
Conference | IEEE Congress on Evolutionary Computation 2006 |
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Abbreviated title | IEEE CEC 2006 |
Country/Territory | Canada |
City | Vancouver |
Period | 16/07/06 → 21/07/06 |
Internet address |
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