Spelling suggestions: "subject:"evolutionary computational"" "subject:"mvolutionary computational""
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[en] SYNTHESIS OF ELECTRONIC CIRCUITS FOR EVOLUTIONARY COMPUTING / [pt] SÍNTESE DE CIRCUITOS ELETRÔNICOS POR COMPUTAÇÃO EVOLUTIVARICARDO SALEM ZEBULUM 06 December 2005 (has links)
[pt] Esta tese investiga a utilização de computação evolutiva
aplicada à síntese de circuitos eletrônicos. A
computação evolutiva compreende uma classe de algoritmos
que utilizam certos aspectos da evolução natural como
metáforas. Particularmente, a seleção natural, a
recombinação de material genético e a mutação são os
mecanismos biológicos nos quais a maior parte destes
algoritmos evolutivos buscam inspiração. Embora
algoritmos evolutivos tenham encontrado em problemas de
otimização o seu maior potencial de aplicação, a
utilização dos mesmos na síntese de circuitos
eletrônicos vem sendo intensamente investigada nos
últimos anos, dando início à área de pesquisa denominada
de Eletrônica Evolutiva. Esta tese enfoca a área de
eletrônica evolutiva sob o ponto de vista de engenharia
de circuitos, e seu maior objetivo é oferecer
embasamento teórico e experimental para proposta de
novas ferramentas de Computer Aided Design (CAD) de
circuitos eletrônicos.
Nesta pesquisa, a utilização de algoritmos evolutivos
não se restringiu àqueles que empregam apenas os três
operadores genéticos descritos anteriormente, isto é,
seleção, recombinação e mutação. Investigou-se a
inclusão de novos métodos e operadores ao fluxo básico
dos algoritmos evolutivos, com o propósito de melhorar
seu desempenho em problemas na área de Eletrônica
Evolutiva. Particularmente, estudou-se a utilização de
complexidade através de sistemas com representação
variável sistemas evolutivos que utilizem como metáfora
o conceito biológico de especiação. Além disso, uma nova
metodologia para otimização com múltiplos objetivos,
baseada em conceitos de aprendizado de Redes Neurais
Artificiais, for também concebida nessa tese.
Realizou-se um amplo estudo de casos, abrangendo
eletrônica analógica, digital e microeletrônica. Uma
grande variedade de circuitos de caráter prático foi
sintetizada, tais como: filtros, amplificadores,
osciladores, retificadores, receptores, comparadores,
multiplexadores e portas digitais básicas. Novos
paradigmas de eletrônica evolutiva foram também
concebidos, com o intuito de tornar os circuitos
projetados competitivos com aqueles convencionalmente
utilizados; estes paradigmas referem-se à forma como os
circuitos são avaliados ao longo do algoritmo evolutivo.
A plataforma para realização dos experimentos consistiu
de simuladores de circuitos e também de circuitos
integrados reconfiguráveis.
Os resultados mostram que esta nova classe de
ferramentas de CAD pode chegar a circuitos mais
eficientes do que os obtidos por ferramentas
convencionais. Além disso, circuitos eletrônicos
sintetizados por computação evolutiva são em geral
bastante distintos daqueles projetados
convencionalmente, o que contribui para a concepção de
novas metodologias de projeto. / [en] This thesis investigates the application of evolutionary
computing techniques in the synthesis of electronic
circuits. Evolutionary computation encompasses a
particular class of algorithm which employ some aspects of
natural evolution as metaphors. Particularly, most of
these algorithms borrow ideas from the natural selection,
genetic material recombination and mutation biological
mechanisms. Even though evolutionary algorithms have been
intensively investigates recently, starting a new research
area called Evolutionary Electronics. This work focuses on
evolutionary electronics from a enginnering perspective
and the main objective is the proposal of a new generation
of a Computer Aided Design (CAD) tools. Many case studies
have been analysed, covering digital and analog
microelectronics. The work aimed the achievement of
competitive results comparing to other CAD tools.
The research has made use of evolutionary algorithms
tailored to these application, by including other genetic
operators besides the ones defined above. The following
methods have been embedded in the evolutionary
methodology: memory based genetic algorithms, use of
variable length representation systems and the use of the
biological speciation metaphor. Furthermore, a new
multiple-objective optimization method, based on
artificial neural networks learning algorithms, has also
been employed in the case studies.
A large number of circuits of practical interest have been
sysnthesised, such as filters, amplifiers, oscillators,
rectifiers, receptors, comparators refer to new approaches
for circuits evaluation, particularly in the digital
domain. Circuit simulators and analog the reconfigurable
circuits have been used as platforms for the evolutionary
process.
The results show that the circuits synthesided through
evolutionary computation are, in some cases, more
efficient than the human designed ones. Besides, the
evolved circuits are usually quite different from their
human designed counterparts, which can contribute to the
creation of new design methodologies.
The author identified many promising ways of evolutionary
algorithms application in analog and digital design, which
may, in the future, overcome conventional design in terms
of area, speed and power consumption.
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Evolution Through The Search For NoveltyLehman, Joel 01 January 2012 (has links)
I present a new approach to evolutionary search called novelty search, wherein only behavioral novelty is rewarded, thereby abstracting evolution as a search for novel forms. This new approach contrasts with the traditional approach of rewarding progress towards the objective through an objective function. Although they are designed to light a path to the objective, objective functions can instead deceive search into converging to dead ends called local optima. As a significant problem in evolutionary computation, deception has inspired many techniques designed to mitigate it. However, nearly all such methods are still ultimately susceptible to deceptive local optima because they still measure progress with respect to the objective, which this dissertation will show is often a broken compass. Furthermore, although novelty search completely abandons the objective, it counterintuitively often outperforms methods that search directly for the objective in deceptive tasks and can induce evolutionary dynamics closer in spirit to natural evolution. The main contributions are to (1) introduce novelty search, an example of an effective search method that is not guided by actively measuring or encouraging objective progress; (2) validate novelty search by applying it to biped locomotion; (3) demonstrate novelty search’s benefits for evolvability (i.e. the ability of an organism to further evolve) in a variety of domains; (4) introduce an extension of novelty search called minimal criteria novelty search that brings a new abstraction of natural evolution to evolutionary computation (i.e. evolution as a search for many ways of iii meeting the minimal criteria of life); (5) present a second extension of novelty search called novelty search with local competition that abstracts evolution instead as a process driven towards diversity with competition playing a subservient role; and (6) evolve a diversity of functional virtual creatures in a single run as a culminating application of novelty search with local competition. Overall these contributions establish novelty search as an important new research direction for the field of evolutionary computation.
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Complementary Layered LearningMondesire, Sean 01 January 2014 (has links)
Layered learning is a machine learning paradigm used to develop autonomous robotic-based agents by decomposing a complex task into simpler subtasks and learns each sequentially. Although the paradigm continues to have success in multiple domains, performance can be unexpectedly unsatisfactory. Using Boolean-logic problems and autonomous agent navigation, we show poor performance is due to the learner forgetting how to perform earlier learned subtasks too quickly (favoring plasticity) or having difficulty learning new things (favoring stability). We demonstrate that this imbalance can hinder learning so that task performance is no better than that of a suboptimal learning technique, monolithic learning, which does not use decomposition. Through the resulting analyses, we have identified factors that can lead to imbalance and their negative effects, providing a deeper understanding of stability and plasticity in decomposition-based approaches, such as layered learning. To combat the negative effects of the imbalance, a complementary learning system is applied to layered learning. The new technique augments the original learning approach with dual storage region policies to preserve useful information from being removed from an agent’s policy prematurely. Through multi-agent experiments, a 28% task performance increase is obtained with the proposed augmentations over the original technique.
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Evolutionary Learning of Boosted Features for Visual Inspection AutomationZhang, Meng 01 March 2018 (has links)
Feature extraction is one of the major challenges in object recognition. Features that are extracted from one type of objects cannot always be used directly for a different type of objects, therefore limiting the performance of feature extraction. Having an automatic feature learning algorithm could be a big advantage for an object recognition algorithm. This research first introduces several improvements on a fully automatic feature construction method called Evolution COnstructed Feature (ECO-Feature). These improvements are developed to construct more robust features and make the training process more efficient than the original version. The main weakness of the original ECO-Feature algorithm is that it is designed only for binary classification and cannot be directly applied to multi-class cases. We also observe that the recognition performance depends heavily on the size of the feature pool from which features can be selected and the ability of selecting the best features. For these reasons, we have developed an enhanced evolutionary learning method for multi-class object classification to address these challenges. Our method is called Evolutionary Learning of Boosted Features (ECO-Boost). ECO-Boost method is an efficient evolutionary learning algorithm developed to automatically construct highly discriminative image features from the training image for multi-class image classification. This unique method constructs image features that are often overlooked by humans, and is robust to minor image distortion and geometric transformations. We evaluate this algorithm with a few visual inspection datasets including specialty crops, fruits and road surface conditions. Results from extensive experiments confirm that ECO-Boost performs closely comparable to other methods and achieves a good balance between accuracy and simplicity for real-time multi-class object classification applications. It is a hardware-friendly algorithm that can be optimized for hardware implementation in an FPGA for real-time embedded visual inspection applications.
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Evolutionary Methodology for Optimization of Image Transforms Subject to Quantization NoisePeterson, Michael Ray 25 June 2008 (has links)
No description available.
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COMPARING AND CONTRASTING THE USE OF REINFORCEMENT LEARNING TO DRIVE AN AUTONOMOUS VEHICLE AROUND A RACETRACK IN UNITY AND UNREAL ENGINE 5Muhammad Hassan Arshad (16899882) 05 April 2024 (has links)
<p dir="ltr">The concept of reinforcement learning has become increasingly relevant in learning- based applications, especially in the field of autonomous navigation, because of its fundamental nature to operate without the necessity of labeled data. However, the infeasibility of training reinforcement learning based autonomous navigation applications in a real-world setting has increased the popularity of researching and developing on autonomous navigation systems by creating simulated environments in game engine platforms. This thesis investigates the comparative performance of Unity and Unreal Engine 5 within the framework of a reinforcement learning system applied to autonomous race car navigation. A rudimentary simulated setting featuring a model car navigating a racetrack is developed, ensuring uniformity in environmental aspects across both Unity and Unreal Engine 5. The research employs reinforcement learning with genetic algorithms to instruct the model car in race track navigation; while the tools and programming methods for implementing reinforcement learning vary between the platforms, the fundamental concept of reinforcement learning via genetic algorithms remains consistent to facilitate meaningful comparisons. The implementation includes logging of key performance variables during run times on each platform. A comparative analysis of the performance data collected demonstrates Unreal Engine's superior performance across the collected variables. These findings contribute insights to the field of autonomous navigation systems development and reinforce the significance of choosing an optimal underlying simulation platform for reinforcement learning applications.</p>
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Applications of Soft ComputingTiwari, A., Knowles, J., Avineri, E., Dahal, Keshav P., Roy, R. January 2006 (has links)
No
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Learning lost temporal fuzzy association rulesMatthews, Stephen January 2012 (has links)
Fuzzy association rule mining discovers patterns in transactions, such as shopping baskets in a supermarket, or Web page accesses by a visitor to a Web site. Temporal patterns can be present in fuzzy association rules because the underlying process generating the data can be dynamic. However, existing solutions may not discover all interesting patterns because of a previously unrecognised problem that is revealed in this thesis. The contextual meaning of fuzzy association rules changes because of the dynamic feature of data. The static fuzzy representation and traditional search method are inadequate. The Genetic Iterative Temporal Fuzzy Association Rule Mining (GITFARM) framework solves the problem by utilising flexible fuzzy representations from a fuzzy rule-based system (FRBS). The combination of temporal, fuzzy and itemset space was simultaneously searched with a genetic algorithm (GA) to overcome the problem. The framework transforms the dataset to a graph for efficiently searching the dataset. A choice of model in fuzzy representation provides a trade-off in usage between an approximate and descriptive model. A method for verifying the solution to the hypothesised problem was presented. The proposed GA-based solution was compared with a traditional approach that uses an exhaustive search method. It was shown how the GA-based solution discovered rules that the traditional approach did not. This shows that simultaneously searching for rules and membership functions with a GA is a suitable solution for mining temporal fuzzy association rules. So, in practice, more knowledge can be discovered for making well-informed decisions that would otherwise be lost with a traditional approach.
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Diagnostic monitoring of dynamic systems using artificial immune systemsMaree, Charl 12 1900 (has links)
Thesis (MScEng (Process Engineering))--University of Stellenbosch, 2006. / The natural immune system is an exceptional pattern recognition system based on
memory and learning that is capable of detecting both known and unknown
pathogens. Artificial immune systems (AIS) employ some of the functionalities of the
natural immune system in detecting change in dynamic process systems. The
emerging field of artificial immune systems has enormous potential in the application
of fault detection systems in process engineering.
This thesis aims to firstly familiarise the reader with the various current methods in
the field of fault detection and identification. Secondly, the notion of artificial immune
systems is to be introduced and explained. Finally, this thesis aims to investigate the
performance of AIS on data gathered from simulated case studies both with and
without noise.
Three different methods of generating detectors are used to monitor various different
processes for anomalous events. These are:
(1) Random Generation of detectors,
(2) Convex Hulls,
(3) The Hypercube Vertex Approach.
It is found that random generation provides a reasonable rate of detection, while
convex hulls fail to achieve the required objectives. The hypercube vertex method
achieved the highest detection rate and lowest false alarm rate in all case studies.
The hypercube vertex method originates from this project and is the recommended
method for use with all real valued systems, with a small number of variables at least.
It is found that, in some cases AIS are capable of perfect classification, where 100%
of anomalous events are identified and no false alarms are generated. Noise has,
expectedly so, some effect on the detection capability on all case studies. The
computational cost of the various methods is compared, which concluded that the
hypercube vertex method had a higher cost than other methods researched. This
increased computational cost is however not exceeding reasonable confines
therefore the hypercube vertex method nonetheless remains the chosen method.
The thesis concludes with considering AIS’s performance in the comparative criteria
for diagnostic methods. It is found that AIS compare well to current methods and that
some of their limitations are indeed solved and their abilities surpassed in certain
cases. Recommendations are made to future study in the field of AIS. Further the
use of the Hypercube Vertex method is highly recommended in real valued scenarios
such as Process Engineering.
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An evolutionary AI-based decision support system for urban regeneration planningYusuf, Syed Adnan January 2010 (has links)
The renewal of derelict inner-city urban districts suffering from high levels of socio-economic deprivation and sustainability problems is one of the key research areas in urban planning and regeneration. Subject to a wide range of social, economical and environmental factors, decision support for an optimal allocation of residential and service lots within such districts is regarded as a complex task. Pre-assessment of various neighbourhood factors before the commencement of actual location allocation of various public services is considered paramount to the sutainable outcome of regeneration projects. Spatial assessment in such derelict built-up areas requires planning of lot assignment for residential buildings in a way to maximize accessibility to public services while minimizing the deprivation of built neighbourhood areas. However, the prediction of socio-economic deprivation impact on the regeneration districts in order to optimize the location-allocation of public service infrastructure is a complex task. This is generally due to the highly conflicting nature of various service structures with various socio-economic and environmental factors. In regards to the problem given above, this thesis presents the development of an evolutionary AI-based decision support systemto assist planners with the assessment and optimization of regeneration districts. The work develops an Adaptive Network Based Fuzzy Inference System (ANFIS) based module to assess neighbourhood districts for various deprivation factors. Additionally an evolutionary genetic algorithms based solution is implemented to optimize various urban regeneration layouts based upon the prior deprivation assessment model. The two-tiered framework initially assesses socio-cultural deprivation levels of employment, health, crime and transport accessibility in neighbourhood areas and produces a deprivation impact matrix overthe regeneration layout lots based upon a trained, network-based fuzzy inference system. Based upon this impact matrix a genetic algorithm is developed to optimize the placement of various public services (shopping malls, primary schools, GPs and post offices) in a way that maximize the accessibility of all services to regenerated residential units as well as contribute to minimize the measure of deprivation of surrounding neighbourhood areas. The outcome of this research is evaluated over two real-world case studies presenting highly coherent results. The work ultimately produces a smart urban regeneration toolkit which provides designer and planner decision support in the form of a simulation toolkit.
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