Abstract
Solution concepts help designing coevolutionary algorithms by interfacing search mechanisms and problems. This work analyses coevolutionary 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 nonsolution 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  16181623 
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 

Abbreviated title  IEEE CEC 2006 
Country  Canada 
City  Vancouver 
Period  16/07/06 → 21/07/06 
Internet address 
