In a "simple" case considered by Vose, the contraction-mapping theorem is replaced by a variant of the Lyapunov stability criterion to describe the convergence of these algorithms when a certain function is linear.
Variance in the "building blocks" of these algorithms gives rise to collateral noise, which usually dominates motion among schemata. In a "simple" case considered by Vose, the contraction-mapping theorem is replaced by a variant of the Lyapunov stability criterion to describe the convergence of these algorithms when a certain function is linear. To guarantee that they converge, an "elitist" operator must often be used, so as not to preserve the distance function. DeJong proposed deterministic crowding to prevent premature convergence; this can also be achieved via a (*) replacement phase with a selection rule based on simulated annealing. These algorithms see use to solve problems with an analytically-insoluble fitness landscape, like the traveling salesman problem. For 10 points, name these algorithms that optimize a fitness function to arrive at a local optimum in a biomimetic manner.
Variance in the "building blocks" of these algorithms gives rise to collateral noise, which usually dominates motion among schemata.
genetic algorithms [prompt on "evolutionary (algorithms)"]