A novel genetic algorithm for evolvable hardware

Evolutionary algorithms are used for solving search and optimization problems. A new field in which they are also applied is evolvable hardware, which refers to a self-configurable electronic system. However, evolvable hardware is not widely recognized as a tool for solving real-world applications, because of the scalability problem, which limits the size of the system that may be evolved. In this paper a new genetic algorithm, particularly designed for evolving logic circuits, is presented and tested for its scalability. The proposed algorithm designs and optimizes logic circuits based on a Programmable Logic Array (PLA) structure. Furthermore it allows the evolution of large logic circuits, without the use of any decomposition techniques. The experimental results, based on the evolution of several logic circuits taken from three different benchmarks, prove that the proposed algorithm is very fast, as only a few generations are required to fully evolve the logic circuits. In addition it optimizes the evolved circuits better than the optimization offered by other evolutionary algorithms based on a PLA and FPGA structures

Generalized disjunction decomposition for evolvable hardware

Evolvable hardware (EHW) refers to self-reconfiguration hardware design, where the configuration is under the control of an evolutionary algorithm (EA). One of the main difficulties in using EHW to solve real-world problems is scalability, which limits the size of the circuit that may be evolved. This paper outlines a new type of decomposition strategy for EHW, the “generalized disjunction decomposition” (GDD), which allows the evolution of large circuits. The proposed method has been extensively tested, not only with multipliers and parity bit problems traditionally used in the EHW community, but also with logic circuits taken from the Microelectronics Center of North Carolina (MCNC) benchmark library and randomly generated circuits. In order to achieve statistically relevant results, each analyzed logic circuit has been evolved 100 times, and the average of these results is presented and compared with other EHW techniques. This approach is necessary because of the probabilistic nature of EA; the same logic circuit may not be solved in the same way if tested several times. The proposed method has been examined in an extrinsic EHW system using the$(1 + lambda)$evolution strategy. The results obtained demonstrate that GDD significantly improves the evolution of logic circuits in terms of the number of generations, reduces computational time as it is able to reduce the required time for a single iteration of the EA, and enables the evolution of larger circuits never before evolved. In addition to the proposed method, a short overview of EHW systems together with the most recent applications in electrical circuit design is provided

Feasibility of the evolutionary algorithm using different behaviours of the mutation rate to design simple digital logic circuits

The evolutionary design of electronic circuits, or evolvable hardware, is a discipline that allows the user to automatically obtain the desired circuit design. The circuit configuration is under the control of evolutionary algorithms. Several researchers have used evolvable hardware to design electrical circuits. Every time that one particular algorithm is selected to carry out the evolution, it is necessary that all its parameters, such as mutation rate, population size, selection mechanisms etc. are tuned in order to achieve the best results during the evolution process. This paper investigates the abilities of evolution strategy to evolve digital logic circuits based on programmable logic array structures when different mutation rates are used. Several mutation rates (fixed and variable) are analyzed and compared with each other to outline the most appropriate choice to be used during the evolution of combinational logic circuits. The experimental results outlined in this paper are important as they could be used by every researcher who might need to use the evolutionary algorithm to design digital logic circuits

Indutseeritud 3-Lie superalgebrad ja nende rakendused superruumis

Väitekirja elektrooniline versioon ei sisalda publikatsiooneKäesoleva doktoritöö eesmärk on uurida selliste n-Lie superalgerbrate omadusi, mis on konstrueeritud kasutades (n-1)-Lie superalgebra aluseks olevat (n-1)-aarset tehet, seda eriti juhul n=3. Tavalise Lie algebra mõistet on võimalik super- (või Z_2-gradueeritud) struktuuridele üle kanda kui toome sisse Lie superalgebra mõiste. Sarnaselt on võimalik n-Lie algebra, kus binaarne tehe on asendatud n-aarse tehtega, üldistada superstruktuuridele, kui kasutame Filippov-Jacobi samasuse gradueeritud analoogi, saades n-Lie superalgebra. Väitekirjas on esitatud madaladimensionaalsete 3-Lie superalgebrate klassifikatsioon. Lisaks näitame, et n-Lie superalgebra abil, mille tehtele leidub superjälg, saab konstrueerida (n+1)-Lie superalgebra, mida me nimetame indutseeritud (n+1)-Lie superalgebraks. Enamgi veel, on tõestatud, et kommutatiivse superalgebra korral on võimalik indutseerida erinevad 3-Lie superalgebra struktuurid kasutades involutsiooni, derivatsiooni või neid mõlemad korraga. Dissertatsioonis on toodud Nambu-Hamiltoni võrrandi üldistus superruumis jaoks, ja on näidatud, et selle abil on võimalik indutseerida ternaarsete Nambu-Poissoni sulgude pere superruumi paarisfunktsioonide jaoks. Järgnevalt on konstrueeritud indutseeritud 3-Lie superalgebrate indutseeritud esitused, kasutades selleks vastavalt kas esialgset binaarset Lie algebrat koos jäljega või Lie superalgebrat koos superjäljega. Töös on näidatud, et 3-Lie algebra indutseeritud esitus on sisestatav jäljeta maatriksite Lie algebrasse sl(V), kus sümboliga V on tähistatud esituse ruum. Kahedimensionaalse indutseeritud esituse korral on esitatud tingimused, mida vastav esitus peab rahuldama, et ta oleks taandumatu.The aim of the present thesis is to study the properties and characteristics of n-Lie superalgebras that are constructed using an operation from (n-1)-Lie superalgebras, especially in the case n=3. A regular Lie algebra can be extended to super- (or Z_2-graded) structures by introducing the notion of Lie superalgebra. Similarly n-Lie algebra, where binary operation is replcaed with n-ary multiplication law, can be extended to superstructures by making use of a graded Filippov-Jacobi identity, giving a notion of n-Lie superalgebra. In the dissertation a classification of low dimensional 3-Lie superalgebras is presented. We show that an n-Lie superalgebra equipped with a supertrace can be used to construct a (n+1)-Lie superalgebra, which is referred to as the induced (n+1)-Lie superalgebra. It is proved that one can construct induced 3-Lie superalgebras from commutative superalgebras by using involution, even degree derivation, or combination of both of them together. In the thesis a generalization of Nambu-Hamilton equation to a superspace is proposed, and it is shown that it induces a family of ternary Nambu-Poisson brackets of even degree functions on a superspace. Finally a representations of induced 3-Lie algebras and Lie superalgebras are constructed by means of a representation of the initial binary Lie algebra and trace or Lie superalgebra and supertrace, respectively. It is shown that the constructed induced representation of 3-Lie algebra is a representation by traceless matrices, that is, lies in the Lie algebra sl(V), where V is a representation space. For 2-dimensional representations the irreduciblility condition of the induced representation of induced 3-Lie algebra is found.https://www.ester.ee/record=b536058

Controlling NMR spin systems for quantum computation

Nuclear magnetic resonance is arguably both the best available quantum technology for implementing simple quantum computing experiments and the worst technology for building large scale quantum computers that has ever been seriously put forward. After a few years of rapid growth, leading to an implementation of Shor's quantum factoring algorithm in a seven-spin system, the field started to reach its natural limits and further progress became challenging. Rather than pursuing more complex algorithms on larger systems, interest has now largely moved into developing techniques for the precise and efficient manipulation of spin states with the aim of developing methods that can be applied in other more scalable technologies and within conventional NMR. However, the user friendliness of NMR implementations means that they remain popular for proof-of-principle demonstrations of simple quantum information protocols

Control of quantum phenomena: Past, present, and future

Quantum control is concerned with active manipulation of physical and chemical processes on the atomic and molecular scale. This work presents a perspective of progress in the field of control over quantum phenomena, tracing the evolution of theoretical concepts and experimental methods from early developments to the most recent advances. The current experimental successes would be impossible without the development of intense femtosecond laser sources and pulse shapers. The two most critical theoretical insights were (1) realizing that ultrafast atomic and molecular dynamics can be controlled via manipulation of quantum interferences and (2) understanding that optimally shaped ultrafast laser pulses are the most effective means for producing the desired quantum interference patterns in the controlled system. Finally, these theoretical and experimental advances were brought together by the crucial concept of adaptive feedback control, which is a laboratory procedure employing measurement-driven, closed-loop optimization to identify the best shapes of femtosecond laser control pulses for steering quantum dynamics towards the desired objective. Optimization in adaptive feedback control experiments is guided by a learning algorithm, with stochastic methods proving to be especially effective. Adaptive feedback control of quantum phenomena has found numerous applications in many areas of the physical and chemical sciences, and this paper reviews the extensive experiments. Other subjects discussed include quantum optimal control theory, quantum control landscapes, the role of theoretical control designs in experimental realizations, and real-time quantum feedback control. The paper concludes with a prospective of open research directions that are likely to attract significant attention in the future.Comment: Review article, final version (significantly updated), 76 pages, accepted for publication in New J. Phys. (Focus issue: Quantum control

Evolution of Robotic Behaviour Using Gene Expression Programming

The main objective in automatic robot controller development is to devise mechanisms whereby robot controllers can be developed with less reliance on human developers. One such mechanism is the use of evolutionary algorithms (EAs) to automatically develop robot controllers and occasionally, robot morphology. This area of research is referred to as evolutionary robotics (ER). Through the use of evolutionary techniques such as genetic algorithms (GAs) and genetic programming (GP), ER has shown to be a promising approach through which robust robot controllers can be developed. The standard ER techniques use monolithic evolution to evolve robot behaviour: monolithic evolution involves the use of one chromosome to code for an entire target behaviour. In complex problems, monolithic evolution has been shown to suffer from bootstrap problems; that is, a lack of improvement in fitness due to randomness in the solution set [103, 105, 100, 90]. Thus, approaches to dividing the tasks, such that the main behaviours emerge from the interaction of these simple tasks with the robot environment have been devised. These techniques include the subsumption architecture in behaviour based robotics, incremental learning and more recently the layered learning approach [55, 103, 56, 105, 136, 95]. These new techniques enable ER to develop complex controllers for autonomous robot. Work presented in this thesis extends the field of evolutionary robotics by introducing Gene Expression Programming (GEP) to the ER field. GEP is a newly developed evolutionary algorithm akin to GA and GP, which has shown great promise in optimisation problems. The presented research shows through experimentation that the unique formulation of GEP genes is sufficient for robot controller representation and development. The obtained results show that GEP is a plausible technique for ER problems. Additionally, it is shown that controllers evolved using GEP algorithm are able to adapt when introduced to new environments. Further, the capabilities of GEP chromosomes to code for more than one gene have been utilised to show that GEP can be used to evolve manually sub-divided robot behaviours. Additionally, this thesis extends the GEP algorithm by proposing two new evolutionary techniques named multigenic GEP with Linker Evolution (mgGEP-LE) and multigenic GEP with a Regulator Gene (mgGEP-RG). The results obtained from the proposed algorithms show that the new techniques can be used to automatically evolve modularity in robot behaviour. This ability to automate the process of behaviour sub-division and optimisation in a modular chromosome is unique to the GEP formulations discussed, and is an important advance in the development of machines that are able to evolve stratified behavioural architectures with little human intervention

Development of parametric CAD models for gradient-based aerodynamic shape optimisation.

PhD Thesis.Shape optimisation is widely used in industry to improve the performance of the product. When performing aerodynamic analysis with CFD (Computational Fluid Dynamics), gradient-based optimisation methods are normally preferred if the number of design variables is high. These methods require the evaluation of the total derivatives, which can be split into two terms: the flow and the shape derivatives. While evaluating the flow derivatives with the adjoint CFD method, this thesis demonstrates that the shape derivatives can be calculated with algorithmically differentiated parametric CAD models. The development of such CAD models allows to compute the derivatives exactly and, by utilising the reverse mode variant of algorithmic differentiation, independently of the number of design parameters. This makes the computation of the shape derivatives efficient and robust. The parametrisation of the test-cases (a cooling channel and a compressor stator blade) is defined by intuitive and designer-friendly variables which capture the shape modes which mainly affect the objective function. The optimised parametric CAD models are compared to reference results. These results are set as the optimal shapes given by parametrisations with refined design space. The reference results of the cooling channel are identified in the literature. For the blade test-case, the design space of the parametric-based CAD model is enlarged (almost quadrupled). The optimised shape obtained with the parametricbased design is able to reproduce the same design modes provided by the enlarged design space. The fit of the assembly constraints of the blade’s test-case (four mounting bolts) during the flow optimisation has never been demonstrated. This is due to the arduous identification of a differentiable assembly constraints’ function. This thesis demonstrates that an approach based on the detection of a signed distance between the blade and the bolts succeeds in fitting the assembly constraints.

Quantum Nescimus: Improving the characterization of quantum systems from limited information

We are currently approaching the point where quantum systems with 15 or more qubits will be controllable with high levels of coherence over long timescales. One of the fundamental problems that has been identified is that, as the number of qubits increases to these levels, there is currently no clear way to use efficiently the information that can be obtained from such a system to make diagnostic inferences and to enable improvements in the underlying quantum gates. Even with systems of only a few bits the exponential scaling in resources required by techniques such as quantum tomography or gate-set tomography will render these techniques impractical. Randomized benchmarking (RB) is a technique that will scale in a practical way with these increased system sizes. Although RB provides only a partial characterization of the quantum system, recent advances in the protocol and the interpretation of the results of such experiments confirm the information obtained as helpful in improving the control and verification of such processes. This thesis examines and extends the techniques of RB including practical analysis of systems affected by low frequency noise, extending techniques to allow the anisotropy of noise to be isolated, and showing how additional gates required for universal computation can be added to the protocol and thus benchmarked. Finally, it begins to explore the use of machine learning to aid in the ability to characterize, verify and validate noise in such systems, demonstrating by way of example how machine learning can be used to explore the edge between quantum non-locality and realism