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.
|Title of host publication||2006 IEEE Congress on Evolutionary Computation, CEC 2006|
|Number of pages||6|
|Publication status||Published - 1 Dec 2006|
|Event||2006 IEEE Congress on Evolutionary Computation, CEC 2006 - Vancouver, BC, Canada|
Duration: 16 Jul 2006 → 21 Jul 2006
|Conference||2006 IEEE Congress on Evolutionary Computation, CEC 2006|
|Period||16/07/06 → 21/07/06|