In recent years, several techniques have been proposed for class-level regression testing. Most of these techniques focus either on whiteor black-box testing, although an integrated approach can have several benefits. As similar tasks have to be carried out for both whiteand black-box testing, an integrated approach can improve efficiency and cost effectiveness. This article explains a new appr...

Black-box optimization algorithms cannot use the specific parameters of the problem instance, i.e., of the fitness function f . Their run time is measured as the number of f -evaluations. This implies that the usual algorithmic complexity of a problem cannot be used in the black-box scenario. Therefore, a new framework for the valuation of algorithms for black-box optimization is presented allo...

Black-box optimization algorithms optimize a fitness function f without knowledge of the specific parameters of the problem instance. Their run time is measured as the number of f -evaluations. This implies that the usual algorithmic complexity of a problem cannot be applied in the black-box scenario. Therefore, a new framework for the valuation of algorithms for black-box optimization is prese...

Black-box complexity theory provides lower bounds for the runtime of black-box optimizers like evolutionary algorithms and other search heuristics and serves as an inspiration for the design of new genetic algorithms. Several black-box models covering different classes of algorithms exist, each highlighting a different aspect of the algorithms under considerations. In this work we add to the ex...

We discuss basic structural properties of finite black box groups. A special emphasis is made on the use of centralisers of involutions in probabilistic recognition of black box groups. In particular, we suggest an algorithm for finding the p-core of a black box group of odd characteristic. This special role of involutions suggest that the theory of black box groups reproduces, at a non-determi...