Ring Migration Topology Helps Bypassing Local Optima

Running several evolutionary algorithms in parallel and occasionally exchanging good solutions is referred to as island models. The idea is that the independence of the different islands leads to diversity, thus possibly exploring the search space better. Many theoretical analyses so far have found a complete (or sufficiently quickly expanding) topology as underlying migration graph most efficient for optimization, even though a quick dissemination of individuals leads to a loss of diversity. We suggest a simple fitness function FORK with two local optima parametrized by $r \geq 2$ and a scheme for composite fitness functions. We show that, while the (1+1) EA gets stuck in a bad local optimum and incurs a run time of $\Theta(n^{2r})$ fitness evaluations on FORK, island models with a complete topology can achieve a run time of $\Theta(n^{1.5r})$ by making use of rare migrations in order to explore the search space more effectively. Finally, the ring topology, making use of rare migrations and a large diameter, can achieve a run time of $\tilde{\Theta}(n^r)$, the black box complexity of FORK. This shows that the ring topology can be preferable over the complete topology in order to maintain diversity.Comment: 12 page

Particle swarm optimization for routing and wavelength assignment in next generation WDM networks.

PhDAll-optical Wave Division Multiplexed (WDM) networking is a promising technology for long-haul backbone and large metropolitan optical networks in order to meet the non-diminishing bandwidth demands of future applications and services. Examples could include archival and recovery of data to/from Storage Area Networks (i.e. for banks), High bandwidth medical imaging (for remote operations), High Definition (HD) digital broadcast and streaming over the Internet, distributed orchestrated computing, and peak-demand short-term connectivity for Access Network providers and wireless network operators for backhaul surges. One desirable feature is fast and automatic provisioning. Connection (lightpath) provisioning in optically switched networks requires both route computation and a single wavelength to be assigned for the lightpath. This is called Routing and Wavelength Assignment (RWA). RWA can be classified as static RWA and dynamic RWA. Static RWA is an NP-hard (non-polynomial time hard) optimisation task. Dynamic RWA is even more challenging as connection requests arrive dynamically, on-the-fly and have random connection holding times. Traditionally, global-optimum mathematical search schemes like integer linear programming and graph colouring are used to find an optimal solution for NP-hard problems. However such schemes become unusable for connection provisioning in a dynamic environment, due to the computational complexity and time required to undertake the search. To perform dynamic provisioning, different heuristic and stochastic techniques are used. Particle Swarm Optimisation (PSO) is a population-based global optimisation scheme that belongs to the class of evolutionary search algorithms and has successfully been used to solve many NP-hard optimisation problems in both static and dynamic environments. In this thesis, a novel PSO based scheme is proposed to solve the static RWA case, which can achieve optimal/near-optimal solution. In order to reduce the risk of premature convergence of the swarm and to avoid selecting local optima, a search scheme is proposed to solve the static RWA, based on the position of swarm‘s global best particle and personal best position of each particle. To solve dynamic RWA problem, a PSO based scheme is proposed which can provision a connection within a fraction of a second. This feature is crucial to provisioning services like bandwidth on demand connectivity. To improve the convergence speed of the swarm towards an optimal/near-optimal solution, a novel chaotic factor is introduced into the PSO algorithm, i.e. CPSO, which helps the swarm reach a relatively good solution in fewer iterations. Experimental results for PSO/CPSO based dynamic RWA algorithms show that the proposed schemes perform better compared to other evolutionary techniques like genetic algorithms, ant colony optimization. This is both in terms of quality of solution and computation time. The proposed schemes also show significant improvements in blocking probability performance compared to traditional dynamic RWA schemes like SP-FF and SP-MU algorithms

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Analysis of Type Three System transport mechanism in gram-negative bacteria

Das Typ III Sekretionssystem (T3SS) ist ein Proteinkomplex den Gramnegative Bakterien nutzen um in einem Schritt Effektorproteine (Effektoren) aus dem Zytosol über die Doppelmembran zu sekretieren. Für viele Bakterien ist das T3SS ein essenzieller Virulenzfaktor, der es ihnen erlaubt mit ihrem Wirt zu interagieren und diesen zu manipulieren. Charakteristisch für das T3SS ist die strukturelle Komponente, der Nadelkomplex. Dieser ähnelt strukturell einer Spritze, deren Basalkörper die bakteriellen Membranen und das Periplasma durchspannt und einer Nadel, die vom Basalkörper aus dem Bakterium ragt. Basierend auf dem Modell einer Spritze wird angenommen, dass Effektoren entfaltet und anschließend durch Basalkörper und Nadelkanal sekretiert werden. Trotz der kontinuierlichen Forschung an T3SS entbehrt dieses Modell einer experimentellen Grundlage und der Mechanismus ist nicht vollständig erklärt. Ziel der Arbeit war es, eine experimentelle Basis für den Sekretionsmechanismus des T3SS zu schaffen. Um zu verstehen, wie das T3SS Effektoren sekretiert, wurden zunächst Fusionsproteine konstruiert, welche aus einem Effektor und einem stabil gefalteten Knotenprotein bestehen. Aufgrund des Knotens in der Fusion ist davon auszugehen, dass dieser während der Sekretion nicht entfalten kann. Die Effektordomäne wird sekretiert während der Knoten im Kanal verbleibt und diesen verstopft. Nach unseremWissen ist diese Arbeit die erste Visualisierung von Effektorfusionen an isolierten Nadelkomplexen. Die Effektorfusion wird N-terminal voran durch den Kanal sekretiert, wobei der Kanal das Substrat umschließt und gegen Proteasen und chemische Modifikationen abschirmt. Die Ergebnisse dieser Arbeit untermauern eine Grundidee der Funktionsweise des T3SS und liefern eine vielversprechende Strategie für in situ-Strukturanalysen. Dieser Ansatz lässt sich auch auf andere Proteinsekretionssysteme übertragen, bei welchen Substrate vor dem Transport entfaltet werden müssen.The Type III Secretion System (T3SS) is a complex used by Gram-negative bacteria to secrete effector proteins from the cytoplasm across the bacterial envelope in a single step. For many pathogens, the T3SS is an essential virulence factor that enables the bacteria to interact with and manipulate their respective host. A characteristic structural feature of the T3SS is the needle complex (NC). The NC resembles a syringe with a basal body spanning both bacterial membranes and a long needle-like structure that protrudes from the bacterium. Based on the paradigm of a syringe-like mechanism, it is generally assumed that effectors are unfolded and secreted from the bacterial cytoplasm through the basal body and needle channel. Despite extensive research on T3SS, this hypothesis lacks experimental evidence and the mechanism of secretion is not fully understood. This work aimed to provide an experimental basis for the model of the T3SS mechanism. In order to elucidate details of the effector secretion mechanism, fusion proteins consisting of an effector and a bulky protein containing a knotted motif were generated. It is assumed that the knot cannot be unfolded during secretion of the chimera. Consequently, these fusions are accepted as T3SS substrates but remain inside the NC channel and obstruct the T3SS. This is, to our best knowledge, the first time effector fusions have been visualized together with isolated NCs and it demonstrates that effector proteins are secreted directly through the channel with their N-terminus first. The channel encloses the substrate and shields it from a protease and chemical modifications. These results corroborate an elementary understanding of how the T3SS works and provide a powerful tool for in situ-structural investigations. This approach might also be applicable to other protein secretion systems that require unfolding of their substrates prior to secretion

Design Space Exploration and Resource Management of Multi/Many-Core Systems

The increasing demand of processing a higher number of applications and related data on computing platforms has resulted in reliance on multi-/many-core chips as they facilitate parallel processing. However, there is a desire for these platforms to be energy-efficient and reliable, and they need to perform secure computations for the interest of the whole community. This book provides perspectives on the aforementioned aspects from leading researchers in terms of state-of-the-art contributions and upcoming trends

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Circuit Design

Circuit Design = Science + Art! Designers need a skilled "gut feeling" about circuits and related analytical techniques, plus creativity, to solve all problems and to adhere to the specifications, the written and the unwritten ones. You must anticipate a large number of influences, like temperature effects, supply voltages changes, offset voltages, layout parasitics, and numerous kinds of technology variations to end up with a circuit that works. This is challenging for analog, custom-digital, mixed-signal or RF circuits, and often researching new design methods in relevant journals, conference proceedings and design tools unfortunately gives the impression that just a "wild bunch" of "advanced techniques" exist. On the other hand, state-of-the-art tools nowadays indeed offer a good cockpit to steer the design flow, which include clever statistical methods and optimization techniques.Actually, this almost presents a second breakthrough, like the introduction of circuit simulators 40 years ago! Users can now conveniently analyse all the problems (discover, quantify, verify), and even exploit them, for example for optimization purposes. Most designers are caught up on everyday problems, so we fit that "wild bunch" into a systematic approach for variation-aware design, a designer's field guide and more. That is where this book can help! Circuit Design: Anticipate, Analyze, Exploit Variations starts with best-practise manual methods and links them tightly to up-to-date automation algorithms. We provide many tractable examples and explain key techniques you have to know. We then enable you to select and setup suitable methods for each design task - knowing their prerequisites, advantages and, as too often overlooked, their limitations as well. The good thing with computers is that you yourself can often verify amazing things with little effort, and you can use software not only to your direct advantage in solving a specific problem, but also for becoming a better skilled, more experienced engineer. Unfortunately, EDA design environments are not good at all to learn about advanced numerics. So with this book we also provide two apps for learning about statistic and optimization directly with circuit-related examples, and in real-time so without the long simulation times. This helps to develop a healthy statistical gut feeling for circuit design. The book is written for engineers, students in engineering and CAD / methodology experts. Readers should have some background in standard design techniques like entering a design in a schematic capture and simulating it, and also know about major technology aspects

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Ancestral Sequence Reconstructions of Stator Proteins of the Bacterial Flagellar Motor

The bacterial flagellar motor (BFM) is a bidirectional nanomachine that confers motility to several bacteria. The BFM is powered by ion transfer across the cell membrane through its stator. The stator consists of two membrane proteins: MotA and MotB in proton (H+)-powered motors or PomA and PomB in sodium (Na+)-powered motors. Over the years, several parts of the BFM have been resolved using numerous mutagenesis studies and different microscopic techniques. However, the entire structure of the BFM, its ion selection mechanism, the functional roles of each structural residue, and how its complexity evolves and adapts over time are not completely known. In this thesis, we used ancestral sequence reconstruction (ASR) to study the evolutionary history and roles of the key structural residues of the stator complex of the BFM. First, we reconstructed and synthesised thirteen combined transmembrane (TM) and plug domains of ancestral MotBs (MotB-ASRs) to test previously hypothesised critical motifs for the ion-selectivity of BFM. The results showed that all resurrected MotB-ASRs were functional and restored motility with the contemporary E. coli MotA in a stator-deleted strain. In addition, all MotB-ASRs exhibited Na+-independent motility in different ionic conditions, suggesting that the synthesised MotB-ASRs were more likely to be proton-powered. Secondly, we reconstructed and synthesised ten complete ancient MotAs (MotA-ASRs) to study the role of the key structural residues of MotA in BFM function. We identified that four of the ten MotA-ASRs were functional and restored motility in combination with contemporary E. coli MotB and several previously synthesised MotB-ASRs. The functional MotA-ASRs also showed Na+-independent motility in different ionic conditions, like our MotB-ASRs. Additionally, the resurrected MotA-ASRs provided evidence of several variable regions of MotA and revealed 30 conserved residues that were essential for flagellar function. Lastly, we screened two novel motility inhibitors, HM2-16F and BB2-50F, and characterised their anti-motility activity on multiple strains and stator types. We also optimised and developed new high-resolution assays for the phenotypic study of stator function to verify the targets of the motility inhibitors. Our results confirmed that these compounds inhibited bacterial swimming but did not target the stator. In summary, this thesis shows the use of ASR as a tool to study the stator proteins of the BFM