350 research outputs found
Fault tolerant and dynamic evolutionary optimization engines
Mimicking natural evolution to solve hard optimization problems has played an important
role in the artificial intelligence arena. Such techniques are broadly classified
as Evolutionary Algorithms (EAs) and have been investigated for around four decades
during which important contributions and advances have been made.
One main evolutionary technique which has been widely investigated is the Genetic
Algorithm (GA). GAs are stochastic search techniques that follow the Darwinian
principle of evolution. Their application in the solution of hard optimization problems
has been very successful. Indeed multi-dimensional problems presenting difficult search
spaces with characteristics such as multi-modality, epistasis, non regularity, deceptiveness,
etc., have all been effectively tackled by GAs.
In this research, a competitive form of GAs known as fine or cellular GAs (cGAs)
are investigated, because of their suitability for System on Chip (SoC) implementation
when tackling real-time problems. Cellular GAs have also attracted the attention
of researchers due to their high performance, ease of implementation and massive
parallelism. In addition, cGAs inherently possess a number of structural configuration
parameters which make them capable of sustaining diversity during evolution and
therefore of promoting an adequate balance between exploitative and explorative stages
of the search.
The fast technological development of Integrated Circuits (ICs) has allowed a considerable
increase in compactness and therefore in density. As a result, it is nowadays
possible to have millions of gates and transistor based circuits in very small silicon
areas. Operational complexity has also significantly increased and consequently other
setbacks have emerged, such as the presence of faults that commonly appear in the
form of single or multiple bit flips. Tough environmental or time dependent operating
conditions can trigger faults in registers and memory allocations due to induced radiation, electron migration and dielectric breakdown. These kinds of faults are known as
Single Event Effects (SEEs).
Research has shown that an effective way of dealing with SEEs consists of a combination
of hardware and software mitigation techniques to overcome faulty scenarios.
Permanent faults known as Single Hard Errors (SHEs) and temporary faults known
as Single Event Upsets (SEUs) are common SEEs. This thesis aims to investigate the
inherent abilities of cellular GAs to deal with SHEs and SEUs at algorithmic level. A
hard real-time application is targeted: calculating the attitude parameters for navigation
in vehicles using Global Positioning System (GPS) technology. Faulty critical
data, which can cause a system’s functionality to fail, are evaluated. The proposed
mitigation techniques show cGAs ability to deal with up to 40% stuck at zero and 30%
stuck at one faults in chromosomes bits and fitness score cells.
Due to the non-deterministic nature of GAs, dynamic on-the-fly algorithmic and
parametric configuration has also attracted the attention of researchers. In this respect,
the structural properties of cellular GAs provide a valuable attribute to influence their
selection pressure. This helps to maintain an adequate exploitation-exploration tradeoff,
either from a pure topological perspective or through genetic operations that also
make use of structural characteristics in cGAs. These properties, unique to cGAs, are
further investigated in this thesis through a set of middle to high difficulty benchmark
problems. Experimental results show that the proposed dynamic techniques enhance
the overall performance of cGAs in most benchmark problems.
Finally, being structurally attached, the dimensionality of cellular GAs is another
line of investigation. 1D and 2D structures have normally been used to test cGAs at
algorithm and implementation levels. Although 3D-cGAs are an immediate extension,
not enough attention has been paid to them, and so a comparative study on the dimensionality
of cGAs is carried out. Having shorter radii, 3D-cGAs present a faster
dissemination of solutions and have denser neighbourhoods. Empirical results reported
in this thesis show that 3D-cGAs achieve better efficiency when solving multi-modal
and epistatic problems. In future, the performance improvements of 3D-cGAs will
merge with the latest benefits that 3D integration technology has demonstrated, such
as reductions in routing length, in interconnection delays and in power consumption
Dynamic and fault tolerant three-dimensional cellular genetic algorithms
In the area of artificial intelligence, the development of Evolutionary Algorithms (EAs) has
been very active, especially in the last decade. These algorithms started to evolve when
scientists from various regions of the world applied the principles of evolution to algorithmic
search and problem solving. EAs have been utilised successfully in diverse complex
application areas. Their success in tackling hard problems has been the engine of the field of
Evolutionary Computation (EC). Nowadays, EAs are considered to be the best solution to
use when facing a hard search or optimisation problem.
Various improvements are continually being made with the design of new operators,
hybrid models, among others. A very important example of such improvements is the use of
parallel models of GAs (PGAs). PGAs have received widespread attention from various
researchers as they have proved to be more effective than panmictic GAs, especially in terms
of efficacy and speedup.
This thesis focuses on, and investigates, cellular Genetic Algorithms (cGAs)-a
competitive variant of parallel GAs. In a cGA, the tentative solutions evolve in overlapped
neighbourhoods, allowing smooth diffusion of the solutions. The benefits derived from using
cGAs come not only from flexibility gains and their fitness to the objective target in
combination with a robust behaviour but also from their high performance and amenability
to implementation using advanced custom silicon chip technologies. Nowadays, cGAs are
considered as adaptable concepts for solving problems, especially complex optimisation
problems. Due to their structural characteristics, cGAs are able to promote an adequate
exploration/exploitation trade-off and thus maintain genetic diversity. Moreover, cGAs are
characterised as being massively parallel and easy to implement.
The structural characteristics inherited in a cGA provide an active area for investigation.
Because of the vital role grid structure plays in determining the effectiveness of the
algorithm, cellular dimensionality is the main issue to be investigated here. The
implementation of cGAs is commonly carried out on a one- or two-dimensional structure.
Studies that investigate higher cellular dimensions are lacking. Accordingly, this research
focuses on cGAs that are implemented on a three-dimensional structure. Having a structure with three dimensions, specifically a cubic structure, facilitates faster spreading of solutions
due to the shorter radius and denser neighbourhood that result from the vertical expansion of
cells. In this thesis, a comparative study of cellular dimensionality is conducted. Simulation
results demonstrate higher performance achieved by 3D-cGAs over their 2D-cGAs
counterparts. The direct implementation of 3D-cGAs on the new advanced 3D-IC
technology will provide added benefits such as higher performance combined with a
reduction in interconnection delays, routing length, and power consumption.
The maintenance of system reliability and availability is a major concern that must be
addressed. A system is likely to fail due to either hard or soft errors. Therefore, detecting a
fault before it deteriorates system performance is a crucial issue. Single Event Upsets
(SEUs), or soft errors, do not cause permanent damage to system functionality, and can be
handled using fault-tolerant techniques. Existing fault-tolerant techniques include hardware
or software fault tolerance, or a combination of both. In this thesis, fault-tolerant techniques
that mitigate SEUs at the algorithmic level are explored and the inherent abilities of cGAs to
deal with these errors are investigated. A fault-tolerant technique and several mitigation
techniques are also proposed, and faulty critical data are evaluated critical fault scenarios
(stuck at ‘1’ and stuck at ‘0’ faults) are taken into consideration. Chief among several test
and real world problems is the problem of determining the attitude of a vehicle using a
Global Positioning System (GPS), which is an example of hard real-time application. Results
illustrate the ability of cGAs to maintain their functionality and give an adequate
performance even with the existence of up to 40% errors in fitness score cells.
The final aspect investigated in this thesis is the dynamic characteristic of cGAs. cGAs,
and EAs in general, are known to be stochastic search techniques. Hence, adaptive systems
are required to continue to perform effectively in a changing environment, particularly when
tackling real-world problems. The adaptation in cellular engines is mainly achieved through
dynamic balancing between exploration and exploitation. This area has received
considerable attention from researchers who focus on improving the algorithmic
performance without incurring additional computational effort.
The structural properties and the genetic operations provide ways to control selection
pressure and, as a result, the exploration/exploitation trade-off. In this thesis, the genetic
operations of cGAs, particularly the selection aspect and their influence on the search
process, are investigated in order to dynamically control the exploration/exploitation trade-off.
Two adaptive-dynamic techniques that use genetic diversity and convergence speeds to
guide the search are proposed. Results obtained by evaluating the proposed approaches on a test bench of diverse-characteristic real-world and test problems showed improvement in
dynamic cGAs performance over their static counterparts and other dynamic cGAs. For
example, the proposed Diversity-Guided 3D-cGA outperformed all the other dynamic cGAs
evaluated by obtaining a higher search success rate that reached to 55%
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
Recent Advances in Wireless Communications and Networks
This book focuses on the current hottest issues from the lowest layers to the upper layers of wireless communication networks and provides "real-time" research progress on these issues. The authors have made every effort to systematically organize the information on these topics to make it easily accessible to readers of any level. This book also maintains the balance between current research results and their theoretical support. In this book, a variety of novel techniques in wireless communications and networks are investigated. The authors attempt to present these topics in detail. Insightful and reader-friendly descriptions are presented to nourish readers of any level, from practicing and knowledgeable communication engineers to beginning or professional researchers. All interested readers can easily find noteworthy materials in much greater detail than in previous publications and in the references cited in these chapters
Niching in Particole Swarm Optimization
The Particle Swarm Optimization (PSO) algorithm, like many optimization algorithms, is designed to find a single optimal solution. When dealing with multimodal functions, it needs some modifications to be able to locate multiple optima. In a parallel with Evolutionary Computation algorithms, these modifications can be grouped in the framework of Niching.
In this thesis, we present a new approach to niching in PSO that is based on clustering particles to identify niches. The neighborhood structure, on which particles rely for communication, is exploited together with the niche information to perform parallel searches to locate multiple optima. The clustering approach was implemented in the k-means based PSO (kPSO), which employs the standard k-means clustering algorithm. We follow the development of kPSO, starting from a first, simple implementation, and then introducing several improvements, such as a mechanism to adaptively identify the number of clusters.
The final kPSO algorithm proves to be a competitive solution when compared with other existing algorithms, since it shows better performance on most multimodal functions in a commonly used benchmark set
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
Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus
This is an open access book. It gathers the first volume of the proceedings of the 31st edition of the International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022, held on June 19 – 23, 2022, in Detroit, Michigan, USA. Covering four thematic areas including Manufacturing Processes, Machine Tools, Manufacturing Systems, and Enabling Technologies, it reports on advanced manufacturing processes, and innovative materials for 3D printing, applications of machine learning, artificial intelligence and mixed reality in various production sectors, as well as important issues in human-robot collaboration, including methods for improving safety. Contributions also cover strategies to improve quality control, supply chain management and training in the manufacturing industry, and methods supporting circular supply chain and sustainable manufacturing. All in all, this book provides academicians, engineers and professionals with extensive information on both scientific and industrial advances in the converging fields of manufacturing, production, and automation
Advances in Solid State Circuit Technologies
This book brings together contributions from experts in the fields to describe the current status of important topics in solid-state circuit technologies. It consists of 20 chapters which are grouped under the following categories: general information, circuits and devices, materials, and characterization techniques. These chapters have been written by renowned experts in the respective fields making this book valuable to the integrated circuits and materials science communities. It is intended for a diverse readership including electrical engineers and material scientists in the industry and academic institutions. Readers will be able to familiarize themselves with the latest technologies in the various fields
Simulated Annealing
The book contains 15 chapters presenting recent contributions of top researchers working with Simulated Annealing (SA). Although it represents a small sample of the research activity on SA, the book will certainly serve as a valuable tool for researchers interested in getting involved in this multidisciplinary field. In fact, one of the salient features is that the book is highly multidisciplinary in terms of application areas since it assembles experts from the fields of Biology, Telecommunications, Geology, Electronics and Medicine
- …