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Submitted by Izabel Franco (izabel-franco@ufscar.br) on 2016-10-03T18:47:50Z No. of bitstreams: 1 DissMAA.pdf: 3325891 bytes, checksum: 1b4744d48d74943990bed42753cc4b4c (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-20T18:27:58Z (GMT) No. of bitstreams: 1 DissMAA.pdf: 3325891 bytes, checksum: 1b4744d48d74943990bed42753cc4b4c (MD5) / Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-20T18:28:04Z (GMT) No. of bitstreams: 1 DissMAA.pdf: 3325891 bytes, checksum: 1b4744d48d74943990bed42753cc4b4c (MD5) / Made available in DSpace on 2016-10-20T18:28:13Z (GMT). No. of bitstreams: 1 DissMAA.pdf: 3325891 bytes, checksum: 1b4744d48d74943990bed42753cc4b4c (MD5) Previous issue date: 2016-03-04 / Não recebi financiamento / The use of technology based on Field Programmable Gate Arrays (FPGAs), a reconfigurable technology, has become a frequent object of study. This technique is feasible and a promising application in the development of embedded systems, however, the difficulty in finding a flexible and efficient way to perform such an application is their bigger problem. In this work, a virtual and reconfigurable architecture (AVR) in FPGA for hardware applications is presented using a Genetic Programming Software on the development of an optimal reconfiguration for this AVR, in order to build a hardware capable of performing a given task in an embedded system. This proposal is a simple, flexible and efficient way to achieve appropriate applications in embedded systems, when compared to other reconfigurable hardware techniques. The representation of phenotype of the proposed evolutionary system is based on a bi-dimensional network function elements (EF). The GPLAB tool for MATLAB is used in Genetic Programming, and the solution found by this procedure is converted into a memory mapping to represent the best solution, where it is used to reconfigure the hardware. In the tests, GPLAB found results for logic circuits in a few generations, and for image filters containing efficient solutions, where there was little hardware occupation, especially memory, in the cases this has been presented, with a reduced chromosome size, shows a proposal efficiency. / O uso da tecnologia baseada em Field Programmable Gate Arrays (FPGAs), de forma reconfigurável, para a solução de diversos problemas atuais, tem se tornado um frequente objeto de estudo. Essa técnica é de aplicação viável e promissora na elaboração de sistemas embarcados, porém, a dificuldade em encontrar uma forma flexível e eficiente de realizar tal aplicação é o seu maior problema. Neste trabalho, é apresentada uma arquitetura virtual e reconfigurável (AVR) em FPGA para aplicações em hardware, utilizando um software de Programação Genética na elaboração de uma reconfiguração ótima para esta AVR, de forma a construir um hardware capaz de efetuar uma determinada tarefa em um sistema embarcado. Esta proposta é uma forma simples, flexível e eficiente de realizar aplicações adequadas em sistemas embarcados, quando comparada a outras técnicas de hardware reconfigurável. A representação do fenótipo no sistema evolutivo proposto se baseia em uma rede de elementos de função (EF) bidimensional. A ferramenta GPLAB, para MATLAB, é usada na Programação Genética, e a solução encontrada por esta é convertida em um mapeamento de memória com o cromossomo da melhor solução, onde este é usado para reconfigurar o hardware. Nos testes realizados, a GPLAB encontrou resultados para circuitos lógicos em poucas gerações, e para filtros de imagem encontrou soluções eficientes, onde ocorreu pouca ocupação de hardware, principalmente da memória nos casos apresentados, apresentando um cromossomo de tamanho reduzido, o que demonstra uma boa eficiência da proposta.

This diploma thesis deals with the use of Cartesian Genetic Programming (CGP) for combinational circuits design. The work addresses the issue of optimizaion of selected logic circuts, arithmetic adders and multipliers, using Cartesian Genetic Programming. The implementation of the CPG is performed in the Python programming language with the aid of NumPy, Numba and Pandas libraries. The method was tested on selected examples and the results were discussed.

The aim of this work is to automatically design a program that is able to detect dyskinetic movement features in the measured patient's movement data. The program will be developed using Cartesian genetic programming equipped with coevolution of fitness predictors. This type of coevolution allows to speed up a design performed by Cartesian genetic programming by evaluating a quality of candidate solutions using only a part of training data. Evolved classifier achieves a performance (in terms of AUC) that is comparable with the existing solution while achieving threefold acceleration of the learning process compared to the variant without the fitness predictors, in average. Experiments with crossover methods for fitness predictors haven't shown a significant difference between investigated methods. However, interesting results were obtained while investigating integer data types that are more suitable for implementation in hardware. Using an unsigned eight-bit data type (uint8_t) we've achieved not only comparable classification performance (for significant dyskinesia AUC = 0.93 the same as for the existing solutions), with improved AUC for walking patient's data (AUC = 0.80, while existing solutions AUC = 0.73), but also nine times speedup of the design process compared to the approach without fitness predictors employing the float data type, in average.

The goal of this work was the study of evolutionary algorithms and utilization of them for digital circuit design. Especially, a genetic programming and its different manipulation with building blocks is mentioned in contrast to a genetic algorithm. On the basis of this approach, I created and tested a hybrid method of electronic circuit design. This method uses spread schemes according to the genetic algorithm for the pattern problems witch are solved by the genetic programming. The method is more successful and have faster convergence to a solution in difficult electronic circuits design than a common algorithm of the genetic programming.

Výzkum prezentovaný v této práci je zaměřen na evoluční optimalizaci vybraných komponent síťových aplikací určených pro monitorovací systémy vysokorychlostních sítí. Práce začíná studiem současných monitorovacích systémů. Jako experimentální platforma byl zvolen systém SDM (Software Defined Monitoring). Detailně bylo analyzováno zpracování síťového provozu, protože tvoří důležitou součást všech monitorovacích systémů. Jako demonstrační komponenty pro aplikaci optimálních technik navržených v této práci byly zvoleny klasifikátor aplikačních protokolů a hashovací funkce pro síťové toky. Evoluční algoritmy byly zkoumány s ohledem nejen na optimalizaci kvality zpracování dat danou síťovou komponentou, ale i na čas potřebný pro výpočet dané komponenty. Byly zkoumány jednokriteriální i vícekriteriální varianty evolučních algoritmů.     Byl navržen nový přístup ke klasifikaci aplikačních protokolů. Přesná i aproximativní verze klasifikátoru byla optimalizována pomocí CGP (Kartézské Genetické Programování). Bylo dosaženo výrazné redukce zdrojů a zpoždění v FPGA (Programovatelné Logické Pole) oproti neoptimalizované verzi. Speciální síťové hashovací funkce byly navrženy pomocí paralelní verze LGP (Lineární Genetické Programování). Tyto hashovací funkce vykazují lepší funkcionalitu oproti moderním hashovacím funkcím. S využitím vícekriteriální optimalizace byly vylepšeny výsledky původní jednokriteriální verze LGP. Paralelní zřetězené verze hashovacích funkcí byly implementovány v FPGA a vyhodnoceny za účelem hashování síťových toků. Nová rekonfigurovatelná hashovací funkce byla navržena jako kombinace vybraných hashovacích funkcí.  Velmi konkurenceschopná obecná hashovací funkce byla rovněž navržena pomocí multikriteriální verze LGP a její funkčnosti byla ověřena na reálných datových sadách v provedených studiích. Vícekriteriální přístup produkuje mírně lepší řešení než jednokriteriální LGP. Také se potvrdilo, že obecné implementace LGP a CGP jsou použitelné pro automatizovaný návrh a optimalizaci vybraných síťových komponent. Je však důležité zvládnout vícekriteriální povahu problému a urychlit časově kritické operace GP

Soft methods of artificial intelligence are often used in the prediction of non-deterministic time series that cannot be modeled using standard econometric methods. These series, such as occur in finance, often undergo changes to their underlying data generation process resulting in inaccurate approximations or requiring additional human judgment and input in the process, hindering the potential for automated solutions. Genetic programming (GP) is a class of nature-inspired algorithms that aims to evolve a population of computer programs to solve a target problem. GP has been applied to time series prediction in finance and other domains. However, most GP-based approaches to these prediction problems do not consider regime change. This paper introduces two new genetic programming modularity techniques, collectively referred to as automatically defined templates, which better enable prediction of time series involving regime change. These methods, based on earlier established GP modularity techniques, take inspiration from software design patterns and are more closely modeled after the way humans actually develop software. Specifically, a regime detection branch is incorporated into the GP paradigm. Regime specific behavior evolves in a separate program branch, implementing the template method pattern. A system was developed to test, validate, and compare the proposed approach with earlier approaches to GP modularity. Prediction experiments were performed on synthetic time series and on the S&P 500 index. The performance of the proposed approach was evaluated by comparing prediction accuracy with existing methods. One of the two techniques proposed is shown to significantly improve performance of time series prediction in series undergoing regime change. The second proposed technique did not show any improvement and performed generally worse than existing methods or the canonical approaches. The difference in relative performance was shown to be due to a decoupling of reusable modules from the evolving main program population. This observation also explains earlier results regarding the inferior performance of genetic programming techniques using a similar, decoupled approach. Applied to financial time series prediction, the proposed approach beat a buy and hold return on the S&P 500 index as well as the return achieved by other regime aware genetic programming methodologies. No approach tested beat the benchmark return when factoring in transaction costs.

When performing predictive data mining, the use of ensembles is claimed to virtually guarantee increased accuracy compared to the use of single models. Unfortunately, the problem of how to maximize ensemble accuracy is far from solved. In particular, the relationship between ensemble diversity and accuracy is not completely understood, making it hard to efficiently utilize diversity for ensemble creation. Furthermore, most high-accuracy predictive models are opaque, i.e. it is not possible for a human to follow and understand the logic behind a prediction. For some domains, this is unacceptable, since models need to be comprehensible. To obtain comprehensibility, accuracy is often sacrificed by using simpler but transparent models; a trade-off termed the accuracy vs. comprehensibility trade-off. With this trade-off in mind, several researchers have suggested rule extraction algorithms, where opaque models are transformed into comprehensible models, keeping an acceptable accuracy.In this thesis, two novel algorithms based on Genetic Programming are suggested. The first algorithm (GEMS) is used for ensemble creation, and the second (G-REX) is used for rule extraction from opaque models. The main property of GEMS is the ability to combine smaller ensembles and individual models in an almost arbitrary way. Moreover, GEMS can use base models of any kind and the optimization function is very flexible, easily permitting inclusion of, for instance, diversity measures. In the experimentation, GEMS obtained accuracies higher than both straightforward design choices and published results for Random Forests and AdaBoost. The key quality of G-REX is the inherent ability to explicitly control the accuracy vs. comprehensibility trade-off. Compared to the standard tree inducers C5.0 and CART, and some well-known rule extraction algorithms, rules extracted by G-REX are significantly more accurate and compact. Most importantly, G-REX is thoroughly evaluated and found to meet all relevant evaluation criteria for rule extraction algorithms, thus establishing G-REX as the algorithm to benchmark against. / <p>Avhandling framlagd 2007-06-01 vid Högskolan i Skövde.</p><p>Opponent: Rögnvaldsson, Thorsteinn, Professor, Sektionen för informationsvetenskap, Data- och Elektroteknik, Högskolan i Halmstad.</p>

Context Free Design Grammar (CFDG) is a programming language for defining recursive structures that can be used to create art. I use CFDG as a design space for genetic programming, experimenting with various options for crossover, mutation, and fitness. In this exploratory work, multiple generations are manually assessed to determine the usefulness of the mutation strategies and fitness functions. I find that simple value mutation and fitness that alters general program structure is not enough to produce an increase of interesting images in CFDG. I discuss these findings as well as future avenues of inquiry for genetic programming in artistic domains.