Martin Schirneck

TL;DR: It is shown that, if good components are sufficiently prevalent in the individuals, majority vote creates an optimal individual with high probability, and this process can be amplified: as long as components are good independently and with probability at least 1/2+δ, it requires only O(log 1/δ + log log n) successive stages of majority vote to create an optimalindividual with high probabilities.

TL;DR: The parameterized complexity of classical problems that arise in the profiling of relational data is studied to give insights into the complexity of enumerating all minimal unique column combinations or functional dependencies.

TL;DR: This article presents a dynamic model of optimizing linear functions under uniform constraints, and investigates the runtimes that different evolutionary algorithms need to recompute an optimal solution when the constraint bound changes by a certain amount.

TL;DR: This paper considers the behavior of the classical ( 1 + 1 ) Evolutionary Algorithm on linear functions under linear constraint and shows tight bounds in the case where the constraint is given by the OneMax function and the objective function is givenBy either the One Max or the BinVal function.

TL;DR: This paper studies the classical (1+1) EA and population-based algorithms and shows that they recompute an optimal solution very efficiently and that a variant of the (1+(λ, λ)) GA can recompute the optimal solution more efficiently in some cases.