Silicon Cluster Optimization Using Extended Compact Genetic Algorithm

Feb 25, 20072 likes1,001 views

This paper presents an efficient cluster optimization algorithm. The proposed algorithm uses extended compact genetic algorithm (ECGA), one of the competent genetic algorithms (GAs) coupled with Nelder-Mead simplex local search. The lowest energy structures of silicon clusters with 4-11 atoms have been successfully predicted. The minimum population size and total number of function (potential energy of the cluster) evaluations required to converge to the global optimum with a reliability of 96% have been empirically determined and are O(n4.2) and O(n8.2) respectively. The results obtained indicate that the proposed algorithm is highly reliable in predicting globally optimal structures. However, certain efficiency techniques have to be employed for predicting structures of larger clusters to reduce the high computational cost due to function evaluation.

Silicon Cluster Optimization Using Extended Compact Genetic Algorithm

This document discusses different techniques for researching target audiences for films, including: mainstream films aimed at a mass market; alternative films produced outside major studios and targeted at niche audiences; and niche films targeted at a small, specific audience. It also lists other factors used in audience research: gender, age, socio-economic status, psychographics, geodemographic segmentation, sexual orientation, and regional identity.

On Optimization of Network-coded Scalable Multimedia Service MulticastingAndrea Tassi

In the near future, the delivery of multimedia multicast services over next-generation networks is likely to become one of the main pillars of future cellular networks. In this extended abstract, we address the issue of efficiently multicasting layered video services by defining a novel optimization paradigm that is based on an Unequal Error Protection implementation of Random Linear Network Coding, and aims to ensure target service coverages by using a limited amount of radio resources.

Optimized Network-coded Scalable Video Multicasting over eMBMS NetworksAndrea Tassi

This document discusses a proposed resource allocation model for delivering scalable video services over 4G/5G networks using network coding. The model aims to maximize the number of users achieving a certain quality of service level while minimizing radio resources. It formulates the problem as an integer optimization and proposes a heuristic algorithm to solve it. Results show the approach can improve service coverage by up to 2.5 times compared to conventional strategies.

Real-Coded Extended Compact Genetic Algorithm based on Mixtures of ModelsPier Luca Lanzi

This document describes a real-coded extended compact genetic algorithm (RECGA) based on mixtures of models. RECGA uses probabilistic models like Bayesian optimization algorithms but with simpler models than typically used in BOAs. It applies an estimation of distribution algorithm approach using probabilistic models to generate new candidate solutions rather than using recombination and mutation operators as in traditional genetic algorithms. The RECGA method is presented as a way to take advantages of both extended compact genetic algorithms and Bayesian optimization algorithms while using less complex probabilistic models.

Parte 1 - Linux ed i sistemi embedded per le reti (di Andrea Tassi)Andrea Tassi

Presentation of 'A Novel Convex Power Adaptation Strategy for Multicast Commu...Andrea Tassi

3GPP's Long Term Evolution (LTE) represents the one of the most valuable alternatives to offer a wireless broadband access in fully mobile network context. In particular LTE is able to manage several communication flows characterized by different QoS constrains. This paper deals with a network topology where the mobile users are clustered in Multicast Groups and the base station broadcasts a different traffic flow to each cluster. In order to improve the network throughput on a per-user basis, all communications rely on a Random Linear Network Coding (RLNC) scheme. A key aspect in the QoS management is represented by the power adaptation strategy in use. This paper proposes a novel convex formulation to the power adaptation problem for the downlink phase taking into account the specific RLNC scheme adopted by each communication flow. By the proposed convex formalization, an optimal solution of the problem can be early found in real time. Moreover, the proposed power adaptation strategy shows good performance for what concern throughput and fairness among the users when compared with other alternatives.

Lecturesaeed alqhtany

Network coding allows intermediate network nodes to mix data by performing random linear combinations of packets. This can increase throughput, provide robustness, and reduce delay and energy consumption in various network scenarios. Key benefits include achieving optimal throughput determined by network capacity as long as nodes receive sufficient independent packets, and enabling opportunistic routing. Network coding is well suited to dynamic networks with limited topology information.

PRINCE2 PractitionerMd. Shahed Rahman (ISMS Lead Auditor, CEH v8)

RLNCAndrea Tassi

The document discusses a proposed system and layer independent network coding architecture design. It describes a methodology for the architectural design that includes network coding as a network function for service-oriented systems. The methodology also includes a proposed architectural design framework. The design is preliminarily validated over LTE and satellite systems. Next steps are discussed.

Sleep Period Optimization Model For Layered Video Service Delivery Over eMBMS...Andrea Tassi

On Extended Compact Genetic Algorithmkknsastry

In this study we present a detailed analysis of the extended compact genetic algorithm (ECGA). Based on the analysis, empirical relations for population sizing and convergence time have been derived and are compared with the existing relations. We then apply ECGA to a non-azeotropic binary working fluid power cycle optimization problem. The optimal power cycle obtained improved the cycle efficiency by 2.5% over that existing cycles, thus illustrating the capabilities of ECGA in solving real-world problems.

R2D2 Project (EP/L006251/1) - Research Objectives & OutcomesAndrea Tassi

This document describes the R2D2 research project funded by EPSRC to investigate network error control techniques for reliable data delivery. The project aims to design novel mathematical frameworks to optimize network-coded architectures for applications requiring ultra-reliable communications and energy efficiency. Specific research activities include optimizing 4G/5G systems for video multicasting, designing efficient rateless decoders, developing sparse network coding schemes, and novel coding schemes for relay networks. Initial results have been presented in 4 conference papers and 1 journal paper.

Parte 3 - Linux ed i sistemi embedded per le reti (di Riccardo Coppola)Andrea Tassi

Sparse Random Network Coding for Reliable Multicast ServicesAndrea Tassi

Point-to-Multipoint communications are expected to play a pivotal role in next-generation networks. This talk refers to a cellular system transmitting layered multicast services to a Multicast Group (MG) of users. Reliability of communications is ensured via different Random Linear Network Coding (RLNC) techniques. We deal with a fundamental problem: the computational complexity of the RLNC decoder. The higher the number of decoding operations is, the more the user's computational overhead grows and, consequently, the faster the batteries of mobile devices drain. By referring to several sparse RLNC techniques, and without any assumption on the implementation of the RLNC decoder in use, we provide an efficient way to characterize the performance of users targeted by ultra-reliable layered multicast services. The proposed modeling allows to efficiently derive the average number of coded packet transmissions needed to recover one or more service layers. We design a convex resource allocation framework that allows to minimize the complexity of the RLNC decoder by jointly optimizing the transmission parameters and the sparsity of the code. The designed optimization framework also ensures service guarantees to predetermined fractions of users. Performance of the proposed optimization framework is then investigated in a LTE-A eMBMS network multicasting H.264/SVC video.

Talk on Resource Allocation Strategies for Layered Multimedia Multicast ServicesAndrea Tassi

The explosive growth of content-on-the-move, such as video streaming to mobile devices, has propelled research on multimedia broadcast and multicast schemes. Multi-rate transmission strategies have been proposed as a means of delivering layered services to users experiencing different downlink channel conditions. In this presentation, we consider random linear network coding for its inherent reliability features and study two encoding approaches, which are appropriate for layered services. We derive packet error probability expressions and use them as performance metrics in the formulation of resource allocation frameworks. The aim of these frameworks is both the optimization of the transmission scheme and the minimization of the number of broadcast packets on each downlink channel, while offering service guarantees to a predetermined fraction of users. Our proposed frameworks are adapted to the LTE stack and the integrated eMBMS technology. We focus on the delivery of a video service based on the H.264/SVC standard and demonstrate the advantages of layered network coding over multi-rate transmission. Furthermore, we establish that the choice of both the network coding technique and the resource allocation method play a critical role in the footprint of a service, as determined by the quality of each received video layer.

A short introduction to Network codingArash Pourdamghani

This document provides an introduction to network coding, including: - Examples of how network coding can increase throughput in butterfly networks and wireless communications - Theories showing how intermediate nodes can linearly combine information to deliver data at the maximum possible rate - Benefits of network coding like increased throughput and efficiency, but also challenges like integrating it into existing infrastructure

Presentation of 'Reliable Rate-Optimized Video Multicasting Services over LTE...Andrea Tassi

In this paper, we propose a novel advanced multi-rate design for evolved Multimedia Multicast/Broadcast Service (eMBMS) in fourth generation (4G) Long-Term Evolution (LTE)/LTE-Advanced (LTE-A) networks. The proposed design provides: i) reliability, based on random network coded (RNC) transmission, and ii) efficiency, obtained by optimized rate allocation across multi-rate RNC streams. The paper provides an in-depth description of the system realization and demonstrates the feasibility of the proposed eMBMS design using both analytical and simulation results. The system performance is compared with popular multi-rate multicast approaches in a realistic simulated LTE/LTE-A environment.

Introduction to Network CodingElectronics & Communication Staff SCU Suez Canal University

This document provides an introduction to network coding. It defines network coding as a method for attaining maximum information flow in a network by considering encoding and decoding of data at network nodes. Random linear network coding is introduced as an approach where coding coefficients are chosen randomly from a finite field, allowing decoding with high probability. Applications of network coding are discussed, including improving efficiency in wireless networks through physical-layer network coding where wireless signals can add up. Potential problems with network coding in practical implementations are also noted.

Simple regenerating codes: Network Coding for Cloud StorageKevin Tong

The document presents Simple Regenerating Codes (SRC) for efficient data repair in cloud storage systems. SRC combines MDS codes for reliability with XOR operations to allow repair using minimal bandwidth and disk I/O. Simulations show SRC reduces storage costs compared to replication and maintains high reliability while improving repair scalability through reduced repair bandwidth and disk accesses.

Acoustic communication using ofdmRAVITEJA828

Genetic Algorithms and Genetic Programming for Multiscale Modelingkknsastry

Effective and efficient multiscale modeling is essential to advance both the science and synthesis in a wide array of fields such as physics, chemistry, materials science, biology, biotechnology and pharmacology. This study investigates the efficacy and potential of using genetic algorithms for multiscale materials modeling and addresses some of the challenges involved in designing competent algorithms that solve hard problems quickly, reliably and accurately.

Towards billion bit optimization via parallel estimation of distribution algo...kknsastry

The document describes research into using efficient estimation of distribution algorithms (EDAs) like the compact genetic algorithm (cGA) to solve optimization problems involving billions of variables. Key aspects discussed include the cGA's memory and computational efficiency through techniques like parallelization and vectorization. The researchers were able to solve a noisy OneMax problem involving over 33 million variables to optimality and a problem with 1.1 billion variables with relaxed convergence. The document argues this research is important because many real-world problems involving nanotechnology, biology, and information systems require solving optimization problems at massive scales.

Empirical Analysis of ideal recombination on random decomposable problemskknsastry

This paper analyzes the behavior of a selectorecombinative genetic algorithm (GA) with an ideal crossover on a class of random additively decomposable problems (rADPs). Specifically, additively decomposable problems of order k whose subsolution fitnesses are sampled from the standard uniform distribution U[0,1] are analyzed. The scalability of the selectorecombinative GA is investigated for 10,000 rADP instances. The validity of facetwise models in bounding the population size, run duration, and the number of function evaluations required to successfully solve the problems is also verified. Finally, rADP instances that are easiest and most difficult are also investigated.

Population sizing for entropy-based model buliding In genetic algorithms kknsastry

This paper presents a population-sizing model for the entropy-based model building in genetic algorithms. Specifically, the population size required for building an accurate model is investigated. The effect of the selection pressure on population sizing is also incorporated. The proposed model indicates that the population size required for building an accurate model scales as Θ(m log m), where m is the number of substructures and proportional to the problem size. Experiments are conducted to verify the derivations, and the results agree with the proposed model.

Let's get ready to rumble redux: Crossover versus mutation head to head on ex...kknsastry

This paper analyzes the relative advantages between crossover and mutation on a class of deterministic and stochastic additively separable problems with substructures of non-uniform salience. This study assumes that the recombination and mutation operators have the knowledge of the building blocks (BBs) and effectively exchange or search among competing BBs. Facetwise models of convergence time and population sizing have been used to determine the scalability of each algorithm. The analysis shows that for deterministic exponentially-scaled additively separable, problems, the BB-wise mutation is more efficient than crossover yielding a speedup of Θ(l logl), where l is the problem size. For the noisy exponentially-scaled problems, the outcome depends on whether scaling on noise is dominant. When scaling dominates, mutation is more efficient than crossover yielding a speedup of Θ(l logl). On the other hand, when noise dominates, crossover is more efficient than mutation yielding a speedup of Θ(l).

Modeling selection pressure in XCS for proportionate and tournament selectionkknsastry

In this paper, we derive models of the selection pressure in XCS for proportionate (roulette wheel) selection and tournament selection. We show that these models can explain the empirical results that have been previously presented in the literature. We validate the models on simple problems showing that, (i) when the model assumptions hold, the theory perfectly matches the empirical evidence; (ii) when the model assumptions do not hold, the theory can still provide qualitative explanations of the experimental results.

Modeling XCS in class imbalances: Population sizing and parameter settingskknsastry

This paper analyzes the scalability of the population size required in XCS to maintain niches that are infrequently activated. Facetwise models have been developed to predict the effect of the imbalance ratio—ratio between the number of instances of the majority class and the minority class that are sampled to XCS—on population initialization, and on the creation and deletion of classifiers of the minority class. While theoretical models show that, ideally, XCS scales linearly with the imbalance ratio, XCS with standard configuration scales exponentially. The causes that are potentially responsible for this deviation from the ideal scalability are also investigated. Specifically, the inheritance procedure of classifiers’ parameters, mutation, and subsumption are analyzed, and improvements in XCS’s mechanisms are proposed to effectively and efficiently handle imbalanced problems. Once the recommendations are incorporated to XCS, empirical results show that the population size in XCS indeed scales linearly with the imbalance ratio.

Substructrual surrogates for learning decomposable classification problems: i...kknsastry

This paper presents a learning methodology based on a substructural classification model to solve decomposable classification problems. The proposed method consists of three important components: (1) a structural model that represents salient interactions between attributes for a given data, (2) a surrogate model which provides a functional approximation of the output as a function of attributes, and (3) a classification model which predicts the class for new inputs. The structural model is used to infer the functional form of the surrogate and its coefficients are estimated using linear regression methods. The classification model uses a maximally-accurate, least-complex surrogate to predict the output for given inputs. The structural model that yields an optimal classification model is searched using an iterative greedy search heuristic. Results show that the proposed method successfully detects the interacting variables in hierarchical problems, group them in linkages groups, and build maximally accurate classification models. The initial results on non-trivial hierarchical test problems indicate that the proposed method holds promise and have also shed light on several improvements to enhance the capabilities of the proposed method.

Let's get ready to rumble redux: Crossover versus mutation head to head on ex...kknsastry

This paper analyzes the relative advantages between crossover and mutation on a class of deterministic and stochastic additively separable problems with substructures of non-uniform salience. This study assumes that the recombination and mutation operators have the knowledge of the building blocks (BBs) and effectively exchange or search among competing BBs. Facetwise models of convergence time and population sizing have been used to determine the scalability of each algorithm. The analysis shows that for deterministic exponentially-scaled additively separable, problems, the BB-wise mutation is more efficient than crossover yielding a speedup of Θ(l logl), where l is the problem size. For the noisy exponentially-scaled problems, the outcome depends on whether scaling on noise is dominant. When scaling dominates, mutation is more efficient than crossover yielding a speedup of Θ(l logl). On the other hand, when noise dominates, crossover is more efficient than mutation yielding a speedup of Θ(l).

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