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Πολιτισμικοί αλγόριθμοι : Εφαρμογή στην ανάλυση της ελληνικότητας του παγκόσμιου ιστούΚατσικούλη, Παναγιώτα 12 October 2013 (has links)
Οι πολιτισμικοί αλγόριθμοι είναι εξελικτικοί αλγόριθμοι εμπνευσμένοι από την κοινωνική εξέλιξη. Περιλαμβάνουν ένα χώρο πεποιθήσεων, ένα πληθυσμό και ένα πρωτόκολλο επικοινωνίας που περιέχει συναρτήσεις που επιτρέπουν την ανταλλαγή γνώσης μεταξύ του πληθυσμού και του χώρου πεποιθήσεων. Στην παρούσα εργασία οι πολιτισμικοί αλγόριθμοι χρησιμοποιούνται για την ανάλυση της ελληνικότητας του παγκόσμιου ιστού. Είναι γνωστό πως η ελληνική γλώσσα αποτελεί πηγή άντλησης πληθώρας λέξεων για τα λεξιλόγια πολλών γλωσσών. Ο παγκόσμιος ιστός αποτελεί πλέον κλαθολικό μέσο επικοινωνίας, χώρο διακίνησης τεράστιου όγκου πληροφορίας και δεδομένων και σύγχρονο μέσο οικονομικής, πολιτικής και κοινωνικής δραστηριοποίησης. Με άλλα λόγια, ο παγκόσμιος ιστός αποτελεί σήμερα το χώρο εκείνο όπου η επίδραση του πολιτισμού, μέσω της γλώσσας, είναι εμφανής στα διάφορα κείμενα που φιλοξενούνται σε αυτόν. Η παρούσα διπλωματικής επιχειρεί να "μετρήσει" το ποσοστό των λέξεων με ελληνική προέλευση που χρησιμοποιούνται στα κάθε είδους κείμενα που εμφανίζονται στις ιστοσελίδες του παγκόσμιου ιστου. Στόχος της εργασίας είναι η διερεύνηση του κατά πόσον είναι εφικτός ο σχεδιασμός κατάλληλου μοντέλου και αντίστοιχων αλγορίθμων που θα επιτρέψουν να εκτιμηθεί η "ελληνικότητα" του παγκόσμιου ιστού. Η μεθοδολογία προσέγγισης του θέματος περιλαμβάνει το σχεδιασμό και την υλοποίηση ενός πολιτισμικού αλγορίθμου και χρήση του περιβάλλοντος προγραμματισμού Python για σχεδιασμό και υλοποίηση κατάλληλης εφαρμογής και για πειραματικό έλεγχο. / Cultural Algorithms are Evolutionary Αlgorithms inspired from societal evolution. They involve a belief space, a population space and a communication protocol which provides functions that enable exchange of knowledge between population and belief space. In this thesis cultural algorithms are used in order to analyze how greek the web is. It is commonly known that the greek language is the source of a plethora of words for other languages' dictionaries. The World Wide Web is, nowadays, a universal means of communication, a place where huge amounts of information and data are transmitted and a modern means of economical, political and social activity. In other words, the world wide web has emerged as a new kind of society. As such, it
has become the place where any culture's in
uence, throuh their language, is obvious in hosted texts. This thesis attempts to "count" the percentage of words with greek origin used in web hosted texts of any kind. The main objective is to investigate whether it is possible to design a proper model and corresponding algorithms that allow to evaluate how greek the web is. The methodology followed in this approach consists of the design and implementation of a Cultural Algorithm and of the use of the programming language Python for designing and implementing a proper application and for experimental evaluation.
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[en] THE OPTIMIZATION OF PETROLEUM FIELD EXPLORATION ALTERNATIVES USING EVOLUTIONARY COMPUTATION / [pt] OTIMIZAÇÃO DE ALTERNATIVAS PARA DESENVOLVIMENTO DE CAMPO DE PETRÓLEO UTILIZANDO COMPUTAÇÃO EVOLUCIONÁRIALUCIANA FALETTI ALMEIDA 21 May 2003 (has links)
[pt] Esta dissertação investiga um sistema baseado em algoritmos
genéticos e algoritmos culturais, aplicado ao processo de
desenvolvimento de um campo de petróleo.
O desenvolvimento de um campo de petróleo consiste, neste
caso, da disposição de poços num reservatório petrolífero,
já conhecido e delimitado, que permita maximizar o Valor
Presente Líquido. Uma disposição de poços define a
quantidade e posição de poços produtores e injetores e do
tipo de poço (horizontalou vertical) a serem empregados no
processo de exploração.
O objetivo do trabalho é avaliar o desempenho de Algoritmos
Genéticos e Algoritmos Culturais como métodos de apoio à
decisão na otimização de alternativas de produção em
reservatórios petrolíferos.
Determinar a localização de novos poços de petróleo em um
reservatório é um problema complexo que depende de
propriedades do reservatório e critérios econômicos, entre
outros fatores. Para que um processo de otimização possa ser
aplicado nesse problema, é necessário definir uma função
objetivo a ser minimizada ou maximizada pelo processo. No
problema em questão, a função objetivo a ser maximizada é o
Valor Presente Líquido (VPL). Para se estabelecer o VPL,
subtrai-se os gastos com a exploração do valor
correspondente ao volume de petróleo estimado da reserva.
Devido à complexidade do perfil de produção de petróleo,
exige-se a utilização de simuladores de reservatório para
esta estimativa. Deste modo, um simulador de reservatórios
é parte integrante da função de avaliação.
O trabalho de pesquisa foi desenvolvido em quatro etapas:
um estudo sobre a área de exploração de petróleo; um estudo
dos modelos da inteligência computacional empregados nesta
área; a definição e implementação de um modelo genético e
cultural para o desenvolvimento de campo petrolífero e o
estudo de caso.
O estudo sobre a área de exploração de campo de petróleo
envolveu a teoria necessária para a construção da função
objetivo.
No estudo sobre as técnicas de inteligência computacional
definiu-se os conceitos principais sobre Algoritmo Genético
e Algoritmo Cultural empregados nesta dissertação.
A modelagem de um Algoritmo Genético e Cultural constitui
no emprego dos mesmos, para que dado um reservatório
petrolífero, o sistema tenha condições de reconhecê-lo e
desenvolvê-lo, ou seja, encontrar a configuração
(quantidade, localização e tipo de poços) que atinja um
maior Valor Presente Líquido.
Os resultados obtidos neste trabalho indicam a viabilidade
da utilização de Algoritmos Genéticos e Algoritmos
Culturais no desenvolvimento de campos de petróleo. / [en] This dissertation investigates a system based in genetic algorithms and cultural algorithms, applied to the
development process of a petroleum field. The development of a petroleum field consists in the placement of wells in an already known and delimited petroleum reservoir, which allows maximizing the Net Present Value. A placement of wells defines the quantity and position of the producing wells, the injecting wells,
and the wells type (horizontal or vertical) to be used in the exploration process. The objective of this work is to evaluate the performance of Genetic Algorithms and Cultural Algorithms as decision support methods on the optimization of production alternatives in petroleum reservoirs. Determining the new petroleum wells location in a reservoir is a complex problem that depends on the properties of the reservoir and on economic criteria, among other factors. In order to an optimization process to be applied to this problem, it s necessary to define a target function to be minimized or maximized by the process. In the given problem, the target function to be maximized is the Net Present Value (NPV). In order to establish the NPV, the exploration cost correspondent to the estimated reservoir petroleum volume is deducted. The complexity of
the petroleum s production profile implies on the use of reservoirs simulators for this estimation. In this way, a reservoir simulator is an integrant part of the evaluation function. The research work was developed in four phases: a study about the petroleum exploration field; a study about the applied computational intelligence models in this area; the definition and implementation of a genetic and cultural model for the development of petroliferous fields and the case study. The study about the petroleum exploration field involved all the necessary theory for the building of the target function. In the study about the computational intelligence techniques, the main concepts about the Genetic Algorithms and Cultural Algorithms applied in this dissertation were defined. The modeling of Genetic and Cultural Algorithms consisted in applying them so that, given a petroleum reservoir, the system is capable of evolve and find configurations (quantity, location and wells type) that achieve greater Net Present Values. The results obtained in this work, indicate that the use of Genetic Algorithms and Cultural Algorithms in the
development of petroleum fields is a promising alternative.
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[en] QUANTUM-INSPIRED EVOLUTIONARY ALGORITHMS FOR PROBLEMS BASED ON NUMERICAL REPRESENTATION / [pt] ALGORITMOS EVOLUTIVOS COM INSPIRAÇÃO QUÂNTICA PARA PROBLEMAS COM REPRESENTAÇÃO NUMÉRICAANDRE VARGAS ABS DA CRUZ 25 September 2007 (has links)
[pt] Desde que foram propostos como método de otimização, os
algoritmos
evolutivos têm sido usados com sucesso para resolver
problemas complexos
nas mais diversas áreas como, por exemplo, o projeto
automático de circuitos
e equipamentos, planejamento de tarefas, engenharia de
software e mineração
de dados, entre tantos outros. Este sucesso se deve, entre
outras coisas, ao fato
desta classe de algoritmos não necessitar de formulações
matemáticas rigorosas
a respeito do problema que se deseja otimizar, além de
oferecer um alto grau
de paralelismo no processo de busca. No entanto, alguns
problemas são computacionalmente
custosos no que diz respeito à avaliação das soluções
durante o
processo de busca, tornando a otimização por algoritmos
evolutivos um processo
lento para situações onde se deseja uma resposta rápida do
algoritmo (como por
exemplo, problemas de otimização online). Diversas
maneiras de se contornar
este problema, através da aceleração da convergência para
boas soluções, foram
propostas, entre as quais destacam-se os Algoritmos
Culturais e os Algoritmos
Co-Evolutivos. No entanto, estes algoritmos ainda têm a
necessidade de avaliar
muitas soluções a cada etapa do processo de otimização. Em
problemas onde
esta avaliação é computacionalmente custosa, a otimização
pode levar um tempo
proibitivo para alcançar soluções ótimas. Este trabalho
propõe um novo algoritmo
evolutivo para problemas de otimização numérica (Algoritmo
Evolutivo
com Inspiração Quântica usando Representação Real - AEIQ-
R), inspirado no
conceito de múltiplos universos da física quântica, que
permite realizar o processo
de otimização com um menor número de avaliações de
soluções. O trabalho
apresenta a modelagem deste algoritmo para a solução de
problemas benchmark
de otimização numérica, assim como no treinamento de redes
neurais
recorrentes em problemas de aprendizado supervisionado de
séries temporais e
em aprendizado por reforço em tarefas de controle. Os
resultados obtidos demonstram
a eficiência desse algoritmo na solução destes tipos de
problemas. / [en] Since they were proposed as an optimization method, the
evolutionary algorithms
have been successfully used for solving complex problems
in several
areas such as, for example, the automatic design of
electronic circuits and equipments,
task planning and scheduling, software engineering and
data mining,
among many others. This success is due, among many other
things, to the fact
that this class of algorithms does not need rigorous
mathematical formulations
regarding the problem to be optimized, and also because it
offers a high degree of
parallelism in the search process. However, some problems
are computationally
intensive when it concerns the evaluation of solutions
during the search process,
making the optimization by evolutionary algorithms a slow
process for situations
where a quick response from the algorithm is desired (for
instance, in online optimization
problems). Several ways to overcome this problem, by
speeding up
convergence time, were proposed, including Cultural
Algorithms and Coevolutionary
Algorithms. However, these algorithms still have the need
to evaluate
many solutions on each step of the optimization process.
In problems where
this evaluation is computationally expensive, the
optimization might take a prohibitive
time to reach optimal solutions. This work proposes a new
evolutionary
algorithm for numerical optimization problems (Quantum-
Inspired Evolutionary
Algorithm for Problems based on Numerical Representation -
QIEA-R),
inspired in the concept of quantum superposition, which
allows the optimization
process to be carried on with a smaller number of
evaluations. The work presents
the modelling for this algorithm for solving benchmark
numerical optimization
problems, and for training recurrent neural networks in
supervised learning and
reinforcement learning. The results show the good
performance of this algorithm
in solving these kinds of problems.
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A study of corporate culture compatibility on supply chain performanceAl-Mutawah, Khalid January 2009 (has links)
Supply chain systems have become a vital component of successful networked business firms/organisations. Over the last three decades, there has been a dramatic growth globally in the formation of supply chain networks. Research, however, indicates that there has been an increase in reported supply chains failures, and the incompatibility issues between participated organisations. Yet, these incompatibility issues are not just technical, but encompass wider cultural, organisational, and economical factors. Whilst research has shown the effect of such factors on supply chain performance, the influence of achieving corporate culture compatibility to the success of supply chains remains poorly understood. This is because it is widely accepted that organisations that operate in the same region possess a similar culture. In contrast, this research will examine the existence of corporate culture diversity between organisations in the same region, rather than diversity of national culture across different regions. Specifically, the study described the development of corporate culture compatibility between supply chains’ organisations and its influences on supply chain performance. Therefore, the thesis focus is the complex interrelationships between corporate culture compatibility of member organisations and supply chain performance. This research identifies cultural norms and beliefs of supply chain members within key organisational factors, rather than national or multi-national organisations factors, as in Hofstede (1983). A multi-method research design (combining case study, simulation, and neuro-fuzzy methods) was used to provide a rounded perspective on the phenomena studied. The multiple case studies helped to explore how corporate culture compatibility influences supply chain performance and develop a conceptual model for this association. The simulation experiments were conducted to verify the obtained conceptual framework from the multiple case studies, and investigate the effects of changing the corporate culture compatibility level on supply chain performance. The simulation is designed based on a Multi-Agent System (MAS) approach, in which each organisation in a supply chain is represented as an intelligent agent. Finally, a neuro-fuzzy approach is presented to assess corporate culture on supply chains context using real data. The analysis of the quantitative neuro-fuzzy study confirmed and validated the theoretical findings and adds depth to our understanding of the influences of corporate culture compatibility on supply chain performance. The study confirmed that organisations within the same supply chain in the same region possess different corporate cultures that consequently need the achievement of corporate culture compatibility as it is indicated by the literature. Moreover, the study revealed two types of corporate culture in supply chains’ context: individual culture and common culture. Individual culture refers to the internal beliefs within the organisation’s boundary, while common culture refers to beliefs when trading with partners across the organisation’s boundary. However, the study shows that common culture has more influences on supply chain performance than individual culture. In addition, the study highlighted bi-directional association between individual culture and common culture that helps the supply chain’s organisations developing their corporate culture compatibility. The results from the current study also showed that supply chain performance was shown to arise dramatically in response to corporate culture compatibility level increases. Yet, this increase in performance is diminished at a higher level of corporate culture compatibility, because more corporate culture compatibility increases are not cost effective for the organisations. In addition, organisations at a higher level of compatibility have more preferences to preserve their individual culture because it represents their identity. Furthermore, the study complements the gap in the literature related to the assessment of corporate culture of individual organisations in supply chains for sustaining a higher supply chain performance. While current culture assessment models observe individual organisations’ culture, the proposed approach describes a single concentrated model that integrates both individual and common culture in measuring influences of culture compatibility on supply chain performance. The findings from this study provide scholars, consultants, managers, and supply chain systems vendors with valuable information. This research thesis contributes to supply chain configuration and partnership formation theory, along with corporate culture theory, and is the first of its kind to establish the use of intelligent methods to model corporate culture compatibility. It is also one of the first empirical studies to compare corporate culture compatibility of supply chains’ organisations from organisational perspectives, rather than national perspectives.
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