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Arquitetura pipeline reconfigurável através de instruções geradas por programação genética para processamento morfológico de imagens digitais utilizando FPGAs; Reconfigurable pipelined architecture through instructions generated by genetic programming for morphological image processing using FPGAs

Pedrino, Emerson Carlos
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 27/11/2008 PT
Relevância na Pesquisa
66.27%
A morfologia matemática fornece ferramentas poderosas para a realização de análise de imagens em baixo nível e tem encontrado aplicações em diversas áreas, tais como: visão robótica, inspeção visual, medicina, análise de textura, entre outras. Muitas dessas aplicações requerem processamento em tempo real e para sua execução de forma eficiente freqüentemente é utilizado hardware dedicado. Também, a tarefa de projetar operadores morfológicos manualmente para uma dada aplicação não é trivial na prática. A programação genética, que é um ramo relativamente novo em computação evolucionária, está se consolidando como um método promissor em aplicações envolvendo processamento de imagens digitais. Seu objetivo primordial é descobrir como os computadores podem aprender a resolver problemas sem, no entanto, serem programados para essa tarefa. Essa área ainda não foi muito explorada no contexto de construção automática de operadores morfológicos. Assim, neste trabalho, desenvolve-se e implementa-se uma arquitetura original, de baixo custo, reconfigurável por meio de instruções morfológicas e lógicas geradas automaticamente através de uma aproximação linear baseada em programação genética, visando-se o processamento morfológico de imagens em tempo real utilizando FPGAs de alta complexidade...

Programação genética: operadores de crossover, blocos construtivos e emergência semântica ; Genetic programming: crossover operators, building blocks and semantic emergence

Inhasz, Rafael
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 19/03/2010 PT
Relevância na Pesquisa
66.14%
Os algoritmos evolutivos são métodos heurísticos utilizados para a solução de problemas de otimização e que possuem mecanismos de busca inspirados nos conceitos da Teoria de Evolução das Espécies. Entre os algoritmos evolutivos mais populares, estão os Algoritmos Genéticos (GA) e a Programação Genética (GP). Essas duas técnicas possuem como ponto em comum o uso pesado do operador de recombinação, ou "crossover" - mecanismo pelo qual novas soluções são geradas a partir da combinação entre soluções existentes. O que as diferencia é a flexibilidade - enquanto que nos algoritmos genéticos as soluções são representadas por códigos binários, na programação genética essa representação é feita por algoritmos que podem assumir qualquer forma ou extensão. A preferência pelo operador de crossover não é simplesmente uma característica em comum das duas técnicas supracitadas, mas um poderoso diferencial. Na medida em que os indivíduos (as soluções) são selecionados de acordo com a respectiva qualidade, o uso do operador crossover tende a aumentar mais rapidamente a qualidade média da população se as partes boas de cada solução combinada (os "building blocks") forem preservadas. Holland [1975] prova matematicamente que sob determinadas condições esse efeito ocorrerá em algoritmos genéticos...

Desambiguação de autores em bibliotecas digitais utilizando redes sociais e programação genética; Author name disambiguation in digital libraries using social networks and genetic programming

Levin, Felipe Hoppe
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Dissertação Formato: application/pdf
POR
Relevância na Pesquisa
66.17%
Bibliotecas digitais tornaram-se uma importante fonte de informação para comunidades científicas. Entretanto, por coletar dados de diferentes fontes, surge o problema de informações ambíguas ou duplicadas de nomes de autores. Métodos tradicionais de desambiguação de nomes utilizam informação sintática de atributos. Todavia, recentemente o uso de redes de relacionamentos, que traz informação semântica, tem sido estudado em desambiguação de dados. Em desambiguação de nomes de autores, relações de co-autoria podem ser usadas para criar uma rede social, que pode ser utilizada para melhorar métodos de desambiguação de nomes de autores. Esta dissertação apresenta um estudo do impacto de adicionar análise de redes sociais a métodos de desambiguação de nomes de autores baseados em informação sintática de atributos. Nós apresentamos uma abordagem de aprendizagem de máquina baseada em Programação Genética e a utilizamos para avaliar o impacto de adicionar análise de redes sociais a desambiguação de nomes de autores. Através de experimentos usando subconjuntos de bibliotecas digitais reais, nós demonstramos que o uso de análise de redes sociais melhora de forma significativa a qualidade dos resultados. Adicionalmente...

An Analysis of Hierarchical Genetic Programming

Rosca, Justinian P.
Fonte: University of Rochester. Computer Science Department. Publicador: University of Rochester. Computer Science Department.
Tipo: Relatório
ENG
Relevância na Pesquisa
66.14%
Hierarchical genetic programming (HGP) approaches rely on the discovery, modification, and use of new functions to accelerate evolution. This paper provides a qualitative explanation of the improved behavior of HGP, based on an analysis of the evolution process from the dual perspective of diversity and causality. From a static point of view, the use of an HGP approach enables the manipulation of a population of higher diversity programs. Higher diversity increases the exploratory ability of the genetic search process, as demonstrated by theoretical and experimental fitness distributions and expanded structural complexity of individuals. From a dynamic point of view, this report analyzes the causality of the crossover operator. Causality relates changes in the structure of an object with the effect of such changes, i.e., changes in the properties or behavior of the object. The analyses of crossover causality suggests that HGP discovers and exploits useful structures in a bottom-up, hierarchical manner. Diversity and causality are complementary, affecting exploration and exploitation in genetic search. Unlike other machine learning techniques that need extra machinery to control the tradeoff between them, HGP automatically trades off exploration and exploitation.

Programação Genética Aplicada no Processo de Descoberta de Conhecimento em Bases de Dados de Redes de Pesquisa.; Genetic Programming Apllied in the Process of Knowledge Discovery in Databases for Research Networks.

DUARTE, Kedma Batista
Fonte: Universidade Federal de Goiás; BR; UFG; Mestrado em Engenharia Elétrica e de Computação; Engenharia Publicador: Universidade Federal de Goiás; BR; UFG; Mestrado em Engenharia Elétrica e de Computação; Engenharia
Tipo: Dissertação Formato: application/pdf
POR
Relevância na Pesquisa
66.17%
The Genetic Programming (GP) is a heuristic algorithm for Data Mining (DM), which can be applied to the classification task. This is a method of evolutionary computing inspired in the mechanisms of natural selection theory of Charles Darwin, declared in 1859 in his book "The Origin of Species." From an initial population, the method search over a number of generations to find solutions adapted to the environment of problem. The PG method was proposed in 1990 by John Koza, who demonstrated in one of its applications, the induction in formation of decision trees in the process of data classification. Within this context, the study developed in this work has as main objective the investigation of the concepts of PG and its application on a database of scientific collaboration networks, helping as a management tool in prospective studies of trends for the establishment of common axes in public policy of Science, Technology and Innovation (STI), focusing on regional development. The method is applied on a set of attributes, sorting them in order to identify similarity relationships between groups of researchers that comprise the network. The study involves the concepts of Knowledge Discovery in Databases (KDD) and Data Mining (DM). Networks of Scientific Collaboration...

Programação Genética Aplicada à Programação de Controladores Lógico Programáveis; Genetic Programming Applied to Scheduling Programmable Logic Controllers

CARNEIRO, Marcos Lajovic
Fonte: Universidade Federal de Goiás; BR; UFG; Mestrado em Engenharia Elétrica e de Computação; Engenharia Publicador: Universidade Federal de Goiás; BR; UFG; Mestrado em Engenharia Elétrica e de Computação; Engenharia
Tipo: Dissertação Formato: application/pdf
POR
Relevância na Pesquisa
66.19%
This research proposes the application of an artificial intelligence technique called genetic programming (GP) to make easier the programming of programmable logical devices (PLC) by the automatic generation of Ladder and Instruction List programs. The system data input can be done by not-specialized people using scenarios composed by time lines. These time lines demonstrate graphically the sequencing details of the PLC input and output permitting the programming of systems that uses memory like inter-locking contacts and the use of timers. Since GP is great dependent of its initial simulation parameters, thousand of simulations have been done to determine the better kind of configuration of cross-over and mutation; Essa pesquisa propõe a aplicação da técnica de inteligência artificial programação genética (PG) para facilitar o trabalho de programação de controladores lógico programáveis (CLP) através da geração automática de programas Ladder e Instruction List. A entrada de dados do sistema de automação é feita de forma leiga a partir de cenários compostos por linhas do tempo. Essas linhas do tempo demonstram graficamente os detalhes do seqüenciamento dos acionamentos das entradas e saídas do CLP permitindo a programação de sistemas que utilizam memória como os inter-travamentos e o uso de temporizadores. Como a PG é altamente dependente dos parâmetros iniciais de simulação...

Programação genética paralela com Pareto: uma ferramenta para modelagem via regressão simbólica; Parallel pareto genetic programming: a tool to modeling via symbolic regression

Marques, Leonardo Garcia
Fonte: Universidade Federal de Uberlândia Publicador: Universidade Federal de Uberlândia
Tipo: Dissertação
POR
Relevância na Pesquisa
66.28%
Indução de programas envolve a descoberta de programas de computador que produzem alguma saída desejada quando estes são submetidos a alguma entrada em particular. Um exemplo é a regressão simbólica, ferramenta de modelagem que busca expressões de funções matemáticas para ajustar determinado conjunto de dados multivariados, mapeando variáveis de entrada para variáveis de saída de controle. A programação genética, uma sub-área da computação evolutiva que usa analogia da teoria da evolução de Darwin e algumas ideias de genética, é uma técnica automática para produzir programas de computador amplamente usada para resolver problemas. No entanto, a implementação da programação genética não é trivial para a maioria dos profissionais, além de demandar alto poder computacional. Este trabalho apresenta uma implementação paralela de programação genética simples de se manusear, otimizada para computadores de arquitetura com múltiplos núcleos e que satisfaz o critério competitivo de simplicidade estrutural e exatidão na predição, através de variação especial multiobjetiva de programação genética, chamada programação genética com Pareto. A implementação proposta tem ganhos de desempenho proporcionais à quantidade de núcleos disponíveis em uso...

Passive Solar Building Design Using Genetic Programming

Oraei Gholami, Mohammad Mahdi
Fonte: Brock University Publicador: Brock University
Tipo: Electronic Thesis or Dissertation
ENG
Relevância na Pesquisa
66.17%
Passive solar building design is the process of designing a building while considering sunlight exposure for receiving heat in winter and rejecting heat in summer. The main goal of a passive solar building design is to remove or reduce the need of mechanical and electrical systems for cooling and heating, and therefore saving energy costs and reducing environmental impact. This research will use evolutionary computation to design passive solar buildings. Evolutionary design is used in many research projects to build 3D models for structures automatically. In this research, we use a mixture of split grammar and string-rewriting for generating new 3D structures. To evaluate energy costs, the EnergyPlus system is used. This is a comprehensive building energy simulation system, which will be used alongside the genetic programming system. In addition, genetic programming will also consider other design and geometry characteristics of the building as search objectives, for example, window placement, building shape, size, and complexity. In passive solar designs, reducing energy that is needed for cooling and heating are two objectives of interest. Experiments show that smaller buildings with no windows and skylights are the most energy efficient models. Window heat gain is another objective used to encourage models to have windows. In addition...

Inverse Illumination Design with Genetic Programming

Moylan, Kelly
Fonte: Brock University Publicador: Brock University
Tipo: Electronic Thesis or Dissertation
ENG
Relevância na Pesquisa
66.17%
Interior illumination is a complex problem involving numerous interacting factors. This research applies genetic programming towards problems in illumination design. The Radiance system is used for performing accurate illumination simulations. Radiance accounts for a number of important environmental factors, which we exploit during fitness evaluation. Illumination requirements include local illumination intensity from natural and artificial sources, colour, and uniformity. Evolved solutions incorporate design elements such as artificial lights, room materials, windows, and glass properties. A number of case studies are examined, including many-objective problems involving up to 7 illumination requirements, the design of a decorative wall of lights, and the creation of a stained-glass window for a large public space. Our results show the technical and creative possibilities of applying genetic programming to illumination design.

Computational complexity analysis of multi-objective genetic programming

Neumann, F.
Fonte: Association for Computing Machinery; online Publicador: Association for Computing Machinery; online
Tipo: Conference paper
Publicado em //2012 EN
Relevância na Pesquisa
66.17%
The computational complexity analysis of genetic programming (GP) has been started recently in [7] by analyzing simple (1+1) GP algorithms for the problems ORDER and MAJORITY. In this paper, we study how taking the complexity as an additional criteria inuences the runtime behavior. We consider generalizations of ORDER and MAJORITY and present a computational complexity analysis of (1+1) GP using multi-criteria fitness functions that take into account the original objective and the complexity of a syntax tree as a secondary measure. Furthermore, we study the expected time until population-based multi-objective genetic programming algorithms have computed the Pareto front when taking the complexity of a syntax tree as an equally important objective.; Frank Neumann

Learning to solve planning problems efficiently by means of genetic programming

Aler, Ricardo; Borrajo, Daniel; Isasi, Pedro
Fonte: Massachusetts Institute of Technology Publicador: Massachusetts Institute of Technology
Tipo: Artigo de Revista Científica Formato: application/pdf
Publicado em //2001 ENG
Relevância na Pesquisa
66.16%
Declarative problem solving, such as planning, poses interesting challenges for Genetic Programming (GP). There have been recent attempts to apply GP to planning that fit two approaches: (a) using GP to search in plan space or (b) to evolve a planner. In this article, we propose to evolve only the heuristics to make a particular planner more efficient. This approach is more feasible than (b) because it does not have to build a planner from scratch but can take advantage of already existing planning systems. It is also more efficient than (a) because once the heuristics have been evolved, they can be used to solve a whole class of different planning problems in a planning domain, instead of running GP for every new planning problem. Empirical results show that our approach (EVOCK) is able to evolve heuristics in two planning domains (the blocks world and the logistics domain) that improve PRODIGY4.0 performance. Additionally, we experiment with a new genetic operator - Instance-Based Crossover - that is able to use traces of the base planner as raw genetic material to be injected into the evolving population.

The effects of transfer of global improvements in genetic programming

Aler, Ricardo; Camacho, David; Moscardini, Alfredo
Fonte: Slovak Academy Sciences Institute of Informatics Publicador: Slovak Academy Sciences Institute of Informatics
Tipo: Artigo de Revista Científica Formato: application/pdf
Publicado em /11/2004 ENG
Relevância na Pesquisa
66.23%
Koza has shown how Automatically Defined Functions (ADFs) can reduce computational effort in the genetic programming paradigm. In Koza’s Automatically Defined Functions, as well as in standard genetic programming, an improvement in a part of a program (an ADF or a main body) can only be transferred to other individuals in the population via crossover. In this article, we consider whether it is a good idea to transfer immediately improvements found by a single individual to other individuals in the population. A system that implements this idea has been proposed and tested for the even-5-parity, even-6-parity, and even-10-parity problems. Results are very encouraging: computational effort is reduced (compared to Koza’s ADFs) and the system seems to be less prone to early stagnation. Also, as evolution occurs in separate populations, our approach permits to parallelize genetic programming in another different way.

Disease-Gene Association Using Genetic Programming

Entezari Heravi, Ashkan
Fonte: Brock University Publicador: Brock University
Tipo: Electronic Thesis or Dissertation
ENG
Relevância na Pesquisa
66.25%
As a result of mutation in genes, which is a simple change in our DNA, we will have undesirable phenotypes which are known as genetic diseases or disorders. These small changes, which happen frequently, can have extreme results. Understanding and identifying these changes and associating these mutated genes with genetic diseases can play an important role in our health, by making us able to find better diagnosis and therapeutic strategies for these genetic diseases. As a result of years of experiments, there is a vast amount of data regarding human genome and different genetic diseases that they still need to be processed properly to extract useful information. This work is an effort to analyze some useful datasets and to apply different techniques to associate genes with genetic diseases. Two genetic diseases were studied here: Parkinson’s disease and breast cancer. Using genetic programming, we analyzed the complex network around known disease genes of the aforementioned diseases, and based on that we generated a ranking for genes, based on their relevance to these diseases. In order to generate these rankings, centrality measures of all nodes in the complex network surrounding the known disease genes of the given genetic disease were calculated. Using genetic programming...

Computational Complexity Analysis of Genetic Programming - Initial Results and Future Directions

Neumann, F.; O'Reilly, U.M.; Wagner, M.
Fonte: Springer; United States Publicador: Springer; United States
Tipo: Parte de Livro
Publicado em //2011 EN
Relevância na Pesquisa
66.24%
The computational complexity analysis of evolutionary algorithmsworking on binary strings has significantly increased the rigorous understanding on how these types of algorithm work. Similar results on the computational complexity of genetic programming would fill an important theoretic gap. They would significantly increase the theoretical understanding on how and why genetic programming algorithms work and indicate, in a rigorous manner, how design choices of algorithm components impact its success. We summarize initial computational complexity results for simple tree-based genetic programming and point out directions for future research.; Frank Neumann, Una-May O’Reilly and Markus Wagner; Genetic and Evolutionary Computation Series

The max problem revisited: the importance of mutation in genetic programming

Kotzing, T.; Sutton, A.; Neumann, F.; O'Reilly, U.M.
Fonte: Association for Computing Machinery; online Publicador: Association for Computing Machinery; online
Tipo: Conference paper
Publicado em //2012 EN
Relevância na Pesquisa
66.3%
This paper contributes to the rigorous understanding of genetic programming algorithms by providing runtime complexity analyses of the well-studied Max problem. Several experimental studies have indicated that it is hard to solve the Max problem with crossover-based algorithms. Our analyses show that different variants of the Max problem can provably be solved using simple mutation-based genetic programming algorithms. Our results advance the body of computational complexity analyses of genetic programming, indicate the importance of mutation in genetic programming, and reveal new insights into the behavior of mutation-based genetic programming algorithms.; Timo Kötzing, Andrew M. Sutton, Frank Neumann and Una-May O'Reilly

A Field Guide to Genetic Programming

Poli, Ricardo; Langdon, William B.; McPhee, Nicholas F.
Fonte: [S.L.] : Lulu Press (lulu.com), 2008. Publicador: [S.L.] : Lulu Press (lulu.com), 2008.
Tipo: Livro Formato: application/pdf
ENG
Relevância na Pesquisa
66.45%
xiv, 233 p. : il. ; 23 cm.; Libro Electrónico; A Field Guide to Genetic Programming (ISBN 978-1-4092-0073-4) is an introduction to genetic programming (GP). GP is a systematic, domain-independent method for getting computers to solve problems automatically starting from a high-level statement of what needs to be done. Using ideas from natural evolution, GP starts from an ooze of random computer programs, and progressively refines them through processes of mutation and sexual recombination, until solutions emerge. All this without the user having to know or specify the form or structure of solutions in advance. GP has generated a plethora of human-competitive results and applications, including novel scientific discoveries and patentable inventions. The authors; Introduction -- Representation, initialisation and operators in Tree-based GP -- Getting ready to run genetic programming -- Example genetic programming run -- Alternative initialisations and operators in Tree-based GP -- Modular, grammatical and developmental Tree-based GP -- Linear and graph genetic programming -- Probalistic genetic programming -- Multi-objective genetic programming -- Fast and distributed genetic programming -- GP theory and its applications -- Applications -- Troubleshooting GP -- Conclusions.; Contents xi 1 Introduction 1.1 Genetic Programming in a Nutshell 1.2 Getting Started 1.3 Prerequisites 1.4 Overview of this Field Guide I Basics 2 Representation...

PAC learning and genetic programming

Kotzing, T.; Neumann, F.; Spohel, R.
Fonte: ACM Press; New York Publicador: ACM Press; New York
Tipo: Conference paper
Publicado em //2011 EN
Relevância na Pesquisa
66.17%
Genetic programming (GP) is a very successful type of learning algorithm that is hard to understand from a theoretical point of view. With this paper we contribute to the computational complexity analysis of genetic programming that has been started recently. We analyze GP in the well-known PAC learning framework and point out how it can observe quality changes in the the evolution of functions by random sampling. This leads to computational complexity bounds for a linear GP algorithm for perfectly learning any member of a simple class of linear pseudo-Boolean functions. Furthermore, we show that the same algorithm on the functions from the same class finds good approximations of the target function in less time.; Timo Kötzing, Frank Neumann and Reto Spöhel

Genetic Programming for Object Detection : a Two-Phase Approach with an Improved Fitness Function

Zhang, Mengjie; Bhowan, Urvesh; Ny, Bunna
Fonte: Universidade Autônoma de Barcelona Publicador: Universidade Autônoma de Barcelona
Tipo: Artigo de Revista Científica Formato: application/pdf
Publicado em //2007 ENG
Relevância na Pesquisa
66.27%
This paper describes two innovations that improve the efficiency and effectiveness of a genetic programming approach to object detection problems. The approach uses genetic programming to construct object detection programs that are applied, in a moving window fashion, to the large images to locate the objects of interest. The first innovation is to break the GP search into two phases with the first phase applied to a selected subset of the training data, and a simplified fitness function. The second phase is initialised with the programs from the first phase, and uses the full set of training data with a complete fitness function to construct the final detection programs. The second innovation is to add a program size component to the fitness function. This approach is examined and compared with a neural network approach on three object detection problems of increasing difficulty. The results suggest that the innovations increase both the effectiveness and the efficiency of the genetic programming search, and also that the genetic programming approach outperforms a neural network approach for the most difficult data set in terms of the object detection accuracy.

Teamwork in genetic programming

LaLena, Michael
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Tese de Doutorado
EN_US
Relevância na Pesquisa
66.22%
This thesis attempts to solve food collection problems using genetic programming. The genetic program will evolve programs that mimic the way ants can collect food and bring it back to a nest. There are two special factors in this genetic program that make the ants work together in order to solve the problem, as opposed to each ant acting on its own. First, there will be a stream in the environment that the ants must cross to get to the food. Although all ants have the same program, some must move into the water and die, building a bridge for the other ants to cross. The surviving ants must realize that a bridge has already been built that they can use, instead of killing themselves by building another bridge. Second, the food will be too heavy for one ant to lift alone. The ants must find the food, and call to other ants for help. If all of the ants are at food waiting for help, some, but not all, of the ants must realize that they are in a deadlock situation, and leave their food to help other ants. Both of these problems require the ants to use teamwork to solve the problem. The ants must realize what other ants are doing, without direct communication or a state machine within the ants.

Building power control and comfort management using genetic programming and fuzzy logic

Ali,Safdar; Kim,DoHyeun
Fonte: Journal of Energy in Southern Africa Publicador: Journal of Energy in Southern Africa
Tipo: Artigo de Revista Científica Formato: text/html
Publicado em 01/05/2015 EN
Relevância na Pesquisa
66.14%
In the last couple of years, energy management in the building environment has been a topic of interest to the research community. A number of renowned methods exist in the literature for energy management in buildings, but the trade-off between occupants comfort level and energy consumption is still a major challenge and needs more attention. In this paper, we propose a power control model for comfort and energy saving, using a fuzzy controller and genetic programming (GP). Our focus is to increase the occupants' comfort index and to minimize the energy consumption simultaneously. First, we implemented a Genetic Algorithm (GA) to optimize the environmental parameters. Second, we control the environment using fuzzy logic and third, we predict the consumed power using GP. The environmental and comfort parameters considered are temperature, illumination and air quality. At the end of the work we compare the power consumption results with and without prediction. The results confirmed the effectiveness of the proposed technique in getting the solution for the above mentioned problem.