Effects of Several Bioinspired Methods on the Stability of Coevolutionary Complexification

We study conditions for sustained growth of complexity in an abstract model of parasitic coevolution. Previous research has found that complexification is hard to achieve if the evolution of the symbiont population is constrained by the hosts but the evolution of the hosts is unconstrained, or, more generally, if the task difficulty is much higher for the symbionts than for the hosts. Here we study whether three bio inspired methods known from previous research on achieving stability in coevolution (balancing, niching, and reduced resistance) can restore complexification in such situations. We find that reduced resistance, and to a lesser degree niching, are successful if applied together with truncation selection, but not if applied together with fitness proportional selection

An approach to sign language translation using the Intel Realsense camera

An Intel RealSense camera is used for translating static manual American Sign Language gestures into text. The system uses palm orientation and finger joint data as inputs for either a support vector machine or a neural network whose architecture has been optimized by a genetic algorithm. A data set consisting of 100 samples of 26 gestures (the letters of the alphabet) is extracted from 10 participants. When comparing the different learners in combination with different standard preprocessing techniques, the highest accuracy of 95% is achieved by a support vector machine with a scaling method, as well as principal component analysis, used for preprocessing. The highest performing neural network system reaches 92.1% but produces predictions much faster. We also present a simple software solution that uses the trained classifiers to enable user-friendly sign language translation

Evolving neural networks to follow trajectories of arbitrary complexity

Many experiments have been performed that use evolutionary algorithms for learning the topology and connection weights of a neural network that controls a robot or virtual agent. These experiments are not only performed to better understand basic biological principles, but also with the hope that with further progress of the methods, they will become competitive for automatically creating robot behaviors of interest. However, current methods are limited with respect to the (Kolmogorov) complexity of evolved behavior. Using the evolution of robot trajectories as an example, we show that by adding four features, namely (1) freezing of previously evolved structure, (2) temporal scaffolding, (3) a homogeneous transfer function for output nodes, and (4) mutations that create new pathways to outputs, to standard methods for the evolution of neural networks, we can achieve an approximately linear growth of the complexity of behavior over thousands of generations. Overall, evolved complexity is up to two orders of magnitude over that achieved by standard methods in the experiments reported here, with the major limiting factor for further growth being the available run time. Thus, the set of methods proposed here promises to be a useful addition to various current neuroevolution methods

Relation between composition, microstructure and oxidation in iron aluminides

The relation between chemical composition, microstructure and oxidation properties has been investigated on various FeAl based alloys, the aim being to induce changes in the microstructure of the compound by selective oxidation of aluminium. Oxidation kinetics that was evaluated on bulk specimens showed that, due to fast diffusion in the alloys, no composition gradient is formed during the aluminium selective oxidation. Accordingly, significant aluminium depletion in the compound could be observed in the thinnest part of oxidised wedge-shape specimens. Another way to obtain samples of variable aluminium content was to prepare diffusion couples with one aluminide and pure iron as end members. These latter specimens have been characterised using electron microscopy and first results of oxidation experiments are presented

Evolving Generalised Maze Solvers

This paper presents a study of the efficacy of comparative controller design methods that aim to produce generalised problem solving behaviours. In this case study, the goal was to use neuro-evolution to evolve generalised maze solving behaviours. That is, evolved robot controllers that solve a broad range of mazes. To address this goal, this study compares objective, non-objective and hybrid approaches to direct the search of a neuro-evolution controller design method. The objective based approach was a fitness function, the non-objective based approach was novelty search, and the hybrid approach was a combination of both. Results indicate that, compared to the fitness function, the hybrid and novelty search evolve significantly more maze solving behaviours that generalise to larger and more difficult maze sets. Thus this research provides empirical evidence supporting novelty and hybrid novelty-objective search as approaches for potentially evolving generalised problem solvers

DJ-1 contributes to adipogenesis and obesity-induced inflammation

Adipose tissue functions as an endocrine organ, and the development of systemic inflammation in adipose tissue is closely associated with metabolic diseases, such as obesity and insulin resistance. Accordingly, the fine regulation of the inflammatory response caused by obesity has therapeutic potential for the treatment of metabolic syndrome. In this study, we analyzed the role of DJ-1 (PARK7) in adipogenesis and inflammation related to obesity in vitro and in vivo. Many intracellular functions of DJ-1, including oxidative stress regulation, are known. However, the possibility of DJ-1 involvement in metabolic disease is largely unknown. Our results suggest that DJ-1 deficiency results in reduced adipogenesis and the down-regulation of pro-inflammatory cytokines in vitro. Furthermore, DJ-1-deficient mice show a low-level inflammatory response in the high-fat diet-induced obesity model. These results indicate previously unknown functions of DJ-1 in metabolism and therefore suggest that precise regulation of DJ-1 in adipose tissue might have a therapeutic advantage for metabolic disease treatment.open0