Johannes Lengler

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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)
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)
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)
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)
In this work we study a diffusion process in a network that consists of two types of vertices: inhibitory vertices (those obstructing the diffusion) and excitatory vertices (those facilitating the diffusion). We consider a continuous time model in which every edge of the network draws its transmission time randomly. For such an asynchronous diffusion(More)
We present a high-capacity model for one-shot association learning (hetero-associative memory) in sparse networks. We assume that basic patterns are pre-learned in networks and associations between two patterns are presented only once and have to be learned immediately. The model is a combination of an Amit-Fusi like network sparsely connected to a Willshaw(More)