Evolutionary Synthesis of Cube Root Computational Circuit Using Graph Hybrid Estimation of Distribution Algorithm

The paper is focused on evolutionary synthesis of analog circuit realization of cube root function using proposed Graph Hybrid Estimation of Distribution Algorithm. The problem of cube root function circuit realization was adopted to demonstrate synthesis capability of the proposed method. Individuals of the population of the proposed method which represent promising topologies are encoded using graphs and hypergraphs. Hybridization with local search algorithm was used. The proposed method employs univariate probabilistic model

De-ossifying the Internet Transport Layer : A Survey and Future Perspectives

ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their useful suggestions and comments.Peer reviewedPublisher PD

In-Materio Extreme Learning Machines

Nanomaterial networks have been presented as a building block for unconventional in-Materio processors. Evolution in-Materio (EiM) has previously presented a way to congure and exploit physical materials for computation, but their ability to scale as datasets get larger and more complex remains unclear. Extreme Learning Machines (ELMs) seek to exploit a randomly initialised single layer feed forward neural network by training the output layer only. An analogy for a physical ELM is produced by exploiting nanomaterial networks as material neurons within the hidden layer. Circuit simulations are used to eciently investigate diode-resistor networks which act as our material neurons. These in-Materio ELMs (iM-ELMs) outperform common classication methods and traditional articial ELMs of a similar hidden layer size. For iM-ELMs using the same number of hidden layer neurons, leveraging larger more complex material neuron topologies (with more nodes/electrodes) leads to better performance, showing that these larger materials have a better capability to process data. Finally, iM-ELMs using virtual material neurons, where a single material is re-used as several virtual neurons, were found to achieve comparable results to iM-ELMs which exploited several dierent materials. However, while these Virtual iM-ELMs provide signicant exibility, they sacrice the highly parallelised nature of physically implemented iM-ELMs

Ambiguity Function Method Scheme for Aircraft Attitude Sensor Utilising GPS/GLONASS Carrier Phase Measurement

When the receivers of GPS, GLONASS, COMPASS and other such systems are equipped with multiple antennas, they can give attitude information. Based on the difference carrier phase equations established in local level frame (LLF), a new algorithm is presented to resolve aircraft attitude determination problems in real-time. Presuming that the cycle integer ambiguity is known, the measurement equations have attitude analytical resolutions using single difference (SD) equations of two navigation satellites in-view. Similar with SD process, the doubledifference (DD) measurements are established and analysed. In addition, the SD and DD algorithms are capable of reducing the integer search space into some discrete point space and then the ambiguity function method (AFM) resolves the ambiguity function within the point solutions space. Therefore the procedures have very low computation, thus saving time. The hardware architecture has been realised using multiple  GPS/GLONASS OEMs. The experimental results have demonstrated that the proposed approach is effective and can satisfy the requirement of real-time application in cases of GPS, and combined GPS, and GLONASS.Defence Science Journal, 2009, 59(5), pp.466-470, DOI:http://dx.doi.org/10.14429/dsj.59.154

Exploiting development to enhance the scalability of hardware evolution.

Evolutionary algorithms do not scale well to the large, complex circuit design problems typical of the real world. Although techniques based on traditional design decomposition have been proposed to enhance hardware evolution's scalability, they often rely on traditional domain knowledge that may not be appropriate for evolutionary search and might limit evolution's opportunity to innovate. It has been proposed that reliance on such knowledge can be avoided by introducing a model of biological development to the evolutionary algorithm, but this approach has not yet achieved its potential. Prior demonstrations of how development can enhance scalability used toy problems that are not indicative of evolving hardware. Prior attempts to apply development to hardware evolution have rarely been successful and have never explored its effect on scalability in detail. This thesis demonstrates that development can enhance scalability in hardware evolution, primarily through a statistical comparison of hardware evolution's performance with and without development using circuit design problems of various sizes. This is reinforced by proposing and demonstrating three key mechanisms that development uses to enhance scalability: the creation of modules, the reuse of modules, and the discovery of design abstractions. The thesis includes several minor contributions: hardware is evolved using a common reconfigurable architecture at a lower level of abstraction than reported elsewhere. It is shown that this can allow evolution to exploit the architecture more efficiently and perhaps search more effectively. Also the benefits of several features of developmental models are explored through the biases they impose on the evolutionary search. Features that are explored include the type of environmental context development uses and the constraints on symmetry and information transmission they impose, genetic operators that may improve the robustness of gene networks, and how development is mapped to hardware. Also performance is compared against contemporary developmental models

Concurrently Evolving Sensor Morphology and Control for a Hexapod Robot

Evolving a robot’s sensor morphology along with its control program has the potential to significantly improve its effectiveness in completing the assigned task, plus accommodates the possibility of allowing it to adapt to significant changes in the environment. In previous work, we presented a learning system where the angle, range, and type of sensors on a hexapod robot, along with the control program, were evolved. The evolution was done in simulation and the tests, which were also done in simulation, showed that effective sensor morphologies and control programs could be co-learned by the system. In this paper, we describe the learning system and show that the simulated results are confirmed by tests on the actual hexapod robot

Response to Changes in Key Stimuli through the Co-Evolution of Sensor Morphology and Control

Co-evolving a robot’s sensor morphology and control program increases the potential that it can effectively complete its tasks and provides a means for adapting to changes in the environment. In previous work, we presented a learning system where the angle, range, and type of sensors on a hexapod robot, along with the control program, were evolved. Although three sensor stimuli were detectable by the system, it used only two due to the relative importance of these two stimuli in completing the task. In the research presented in this paper, we used the same system, but reduced the availability of a key stimuli; the most effective solution now required the use of all three stimuli. The learning system still performed well by pacing sensors appropriate for the third stimuli and creating a program that utilized these sensors to successfully solve the problem

Four-leg active power filter control with SUI-PI controller

Four-leg active power filter is considered one of the greatest vital active filters that are frequently used in industrial applications, especially those that need to be controlled in each individual phase. Also, to control the neutral current that created because of a lot of unbalanced and non-linear loads. In this paper, the used active filter was controlled by a proposed control method which can achieve simplicity and intelligence at the same time. The novelty of this paper is using the proposed controller with Four-leg active power filter. This controller relies on instantaneous reactive power theory, which used to create the required currents that are injected into the network via the used active filter to remove the problems created by unbalanced and non-linear loads. It is also maintained that the current source a pure sinusoidal wave. The system is implemented on MATLAB/Simulink. The simulation results proved the preference of the proposed controller than the conventional proportional-integration controller, where it reduced the percentage of total harmonic distortion for the current source

Grand Challenge 7: Journeys in Non-Classical Computation

We review progress in Grand Challenge 7 : Journeys in Non-Classical Computation. We overview GC7-related events, review some background work in certain aspects of GC7 (hypercomputation, bio-inspired computation, and embodied computation) and identify some of the unifying challenges. We review the progress in implementations of one class of non-classical computers: reaction-diffusion systems. We conclude with warnings about “regression to the classical”

Evolution of Transistor Circuits

Der Entwurf von analogen Schaltungen ist ein Bereich der Elektronikentwicklung, der dem Entwickler ein hohes Maß an Wissen und Kreativität beim Lösen von Problemen abverlangt. Bis heute gibt es nur rudimentäre analytische Lösungen um die Bauteile von Schaltungen zu dimensionieren. Motiviert durch diese Herausforderungen, konzentriert sich diese Arbeit auf die automatische Synthese analoger Schaltungen mit Hilfe von Evolutionären Algorithmen. Als analoges Substrat wird ein FPTA benutzt, das ein Feld von konfigurierbaren Transistoren zur Verfügung stellt. Der Einsatz von echter Hardware bietet zwei Vorteile: erstens können entstehende Schaltungen schneller getestet werden als mit einem Simulator und zweitens funktionieren die gefundenen Schaltungen garantiert auf einem echten Chip. Softwareseitig eignen sich Evolutionäre Algorithmen besonders gut für die Synthese analoger Schaltungen, da sie keinerlei Vorwissen über das Optimierungsproblem benötigen. In dieser Arbeit werden neue genetische Operatoren entwickelt, die das Verständnis von auf dem FPTA evolutionierten Schaltungen erleichtern und außerdem Lösungen finden sollen, die auch außerhalb des Substrates funktionieren. Dies ist mit der Hoffnung verbunden, möglicherweise neue und ungewöhnliche Schaltungsprinzipien zu entdecken. Weiterhin wird ein mehrzieliger Optimierungsalgorithmus implementiert und verfeinert, um die Vielzahl von Variablen berücksichtigen zu können, die für die gleichzeitige Optimierung von Topologie und Bauteiledimensionierung notwendig sind. Die vorgeschlagenen genetischen Operatoren, sowie die mehrzielige Optimierung werden für die Evolution von logischen Gattern, Komparatoren, Oszillatoren und Operationsverstärkern eingesetzt. Der Ressourcenverbrauch der durch Evolution gefundenen Schaltungen wird damit vermindert und es ist möglich in einigen Fällen einen übersichtlichen Schaltplan zu erstellen. Ein modulares System für die Evolution von Schaltungen auf konfigurierbaren Substraten wurde entwickelt. Es wird gezeigt, dass mit diesem System FPTA-Architekturen modelliert und direkt für Evolutionsexperimente verwendet werden können