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In this article, we formulate for the first time the notion of a quantum evolutionary algorithm. In fact we define a quantum analogue for any elitist (1+1) randomized search heuristic. The quantum evolutionary algorithm, which we call <i>(1+1) quantum evolutionary algorithm</i> (QEA), is the quantum version of the classical (1+1) evolutionary algorithm… (More)

For the theoretical study of real-world networks, we propose a model of scale-free random graphs with underlying geometry that we call geometric inhomogeneous random graphs (GIRGs). GIRGs generalize hyperbolic random graphs, which are a popular model to test algorithms for social and technological networks. Our generalization overcomes some limitations of… (More)

Black-box complexity is a complexity theoretic measure for how difficult a problem is to be optimized by a general purpose optimization algorithm. It is thus one of the few means trying to understand which problems are tractable for genetic algorithms and other randomized search heuristics. Most previous work on black-box complexity is on artificial test… (More)

- Johannes Lengler, Tobias Bittner, Doris Münster, Alaa El-Din A Gawad, Jochen Graw
- Ophthalmic research
- 2005

Sox2 transcription factor is expressed in neural tissues and sensory epithelia from the early stages of development. Particularly, it is known to activate crystallin gene expression and to be involved in differentiation of lens and neural tissues. However, its place in the signaling cascade is not well understood. Here, we report about the response of its… (More)

Black-box complexity theory provides lower bounds for the runtime %classes of black-box optimizers like evolutionary algorithms 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… (More)

As in classical runtime analysis the OneMax problem is the most prominent test problem also in black-box complexity theory. It is known that the unrestricted, the memory-restricted, and the ranking-based black-box complexities of this problem are all of order n/log n, where n denotes the length of the bit strings. The combined memory-restricted… (More)

1 Neuronal simulations fall in two broad classes: ones that use spiking neurons and ones that don't. While spiking models match biology better than rate-based systems, computationally they can be quite expensive. The literature offers some attempts to find and use rate-based neuron models that capture important properties of spiking units. One of the most… (More)

In Chung-Lu random graphs, a classic model for real-world networks, each vertex is equipped with a weight drawn from a power-law distribution (for which we fix an exponent 2 < β < 3), and two vertices form an edge independently with probability proportional to the product of their weights. Modern, more realistic variants of this model also equip each vertex… (More)

- Johannes Lengler, Gunter Malle
- 2010

a r t i c l e i n f o a b s t r a c t In number theory, great efforts have been undertaken to study the Cohen–Lenstra probability measure on the set of all finite abelian p-groups. On the other hand, group theorists have studied a probability measure on the set of all partitions induced by the probability that a randomly chosen n × n-matrix over F p is… (More)

We present a fixed budget analysis of the (1+1) evolutionary algorithm for general linear functions, considering both the quality of the solution after a predetermined 'budget' of fitness function evaluations (a priori) and the improvement in quality when the algorithm is given additional budget, given the quality of the current solution (a posteriori). Two… (More)