Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Electrical and Computer Engineering, 2008. / Includes bibliographical references.

Thesis (Ph. D.)--Florida State University, 2006. / Advisor: Xin Yuan, Florida State University, College of Arts and Sciences, Dept. of Computer Science. Title and description from dissertation home page (viewed Sept. 19, 2006). Document formatted into pages; contains xiii, 162 pages. Includes bibliographical references.

O estilo de aprendizagem indica as preferências do sujeito em relação às estratégias de aprendizagem que podem ser empregadas pelo professor na construção do conhecimento. O presente trabalho tem por objetivo investigar se o prévio conhecimento dos estilos de aprendizagem pelos alunos e pelo professor contribui para o alinhamento de esforços aplicados no processo de ensino e aprendizagem e, conseqüentemente, para a melhoria do resultado alcançado. No estudo de caso realizado em três turmas de algoritmos e programação, partiu-se do pressuposto de que as estratégias pedagógicas adotadas pelo professor levam à sinergia desde que os estilos de aprendizagem dos sujeitos sejam identificados; a metodologia de trabalho empregada, na consecução dos objetivos, considere os estilos de aprendizagem predominantes na turma; e, haja uma boa relação interpessoal entre o professor e os alunos. A partir da análise que se fez, verificou-se a interdependência entre os estilos de aprendizagem predominantes e as estratégias pedagógicas adotadas pelos professores, assim como foram identificados indícios de atividades de ensino e aprendizagem observáveis que denotam coerência com os estilos mapeados. Logo, os resultados inferidos, a partir do estudo de casos empregado neste trabalho, reafirmam a expectativa geral dos benefícios do conhecimento sobre estilos de aprendizagem para as práticas educacionais, principalmente no que tange à melhoria dos métodos de ensino e à qualificação das ações do professor. / The learning style indicates the preferences of the subject with regard to the learning strategies that could be used by the teacher in the construction of knowledge. The goal of the present paper is to investigate if previous knowledge of the learning styles by the students and by the teacher contributes towards aligning the efforts applied on the teaching and learning process and, consequently, towards improving the achieved result. In the case study conducted in three algorithm and programming groups, it was assumed that the pedagogical strategies employed by the teacher lead to synergy, provided the learning styles of the subjects are identified; the methodology used for achieving the goals, takes into consideration the predominant learning styles of the group; and the existence of a good interpersonal relationship between the teacher and the students. The analysis of the issue pointed to an interdependence between the predominant learning styles and the pedagogical strategies employed by the teachers, and other identifications include hints of observable teaching and learning activities that show coherence with the mapped styles. Therefore, the results accomplished, based on the study cases of this paper, reaffirm the general expectation of the benefits of knowledge over the learning styles for educational activities, especially with regard to an improvement of the teaching methods and the qualification of teacher actions.

O estilo de aprendizagem indica as preferências do sujeito em relação às estratégias de aprendizagem que podem ser empregadas pelo professor na construção do conhecimento. O presente trabalho tem por objetivo investigar se o prévio conhecimento dos estilos de aprendizagem pelos alunos e pelo professor contribui para o alinhamento de esforços aplicados no processo de ensino e aprendizagem e, conseqüentemente, para a melhoria do resultado alcançado. No estudo de caso realizado em três turmas de algoritmos e programação, partiu-se do pressuposto de que as estratégias pedagógicas adotadas pelo professor levam à sinergia desde que os estilos de aprendizagem dos sujeitos sejam identificados; a metodologia de trabalho empregada, na consecução dos objetivos, considere os estilos de aprendizagem predominantes na turma; e, haja uma boa relação interpessoal entre o professor e os alunos. A partir da análise que se fez, verificou-se a interdependência entre os estilos de aprendizagem predominantes e as estratégias pedagógicas adotadas pelos professores, assim como foram identificados indícios de atividades de ensino e aprendizagem observáveis que denotam coerência com os estilos mapeados. Logo, os resultados inferidos, a partir do estudo de casos empregado neste trabalho, reafirmam a expectativa geral dos benefícios do conhecimento sobre estilos de aprendizagem para as práticas educacionais, principalmente no que tange à melhoria dos métodos de ensino e à qualificação das ações do professor. / The learning style indicates the preferences of the subject with regard to the learning strategies that could be used by the teacher in the construction of knowledge. The goal of the present paper is to investigate if previous knowledge of the learning styles by the students and by the teacher contributes towards aligning the efforts applied on the teaching and learning process and, consequently, towards improving the achieved result. In the case study conducted in three algorithm and programming groups, it was assumed that the pedagogical strategies employed by the teacher lead to synergy, provided the learning styles of the subjects are identified; the methodology used for achieving the goals, takes into consideration the predominant learning styles of the group; and the existence of a good interpersonal relationship between the teacher and the students. The analysis of the issue pointed to an interdependence between the predominant learning styles and the pedagogical strategies employed by the teachers, and other identifications include hints of observable teaching and learning activities that show coherence with the mapped styles. Therefore, the results accomplished, based on the study cases of this paper, reaffirm the general expectation of the benefits of knowledge over the learning styles for educational activities, especially with regard to an improvement of the teaching methods and the qualification of teacher actions.

Orientador: Flavio Keidi Miyazawa / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-09T00:31:09Z (GMT). No. of bitstreams: 1 Vignatti_AndreLuis_M.pdf: 1110014 bytes, checksum: 4a8c19589a3914eb255c6938623be094 (MD5) Previous issue date: 2006 / Resumo: Neste trabalho estudamos a interação entre duas áreas: otimização combinatória e compartilhamento de custos (cost-sharing), que é a arte de dividir os custos associados a construção e manutenção de uma solução a qual um grupo de usuários é beneficiado. Apresentamos algoritmos para problemas de projeto de redes, tendo como objetivo principal os problemas ¿Connected Facility Location¿ e ¿Rent-or-Buy¿. Estes dois problemas são NP-difíceis, pois têm como caso particular o problema da arvore mínima de Steiner, que tambem é NP-dificil. Na primeira parte do trabalho, temos a seguinte questão como motivação: ¿Como projetar uma boa rede, ou seja, uma rede que satisfaça todas as propriedades do problema e ao mesmo tempo minimize o custo de construção desta rede?¿ 'E nesta parte que os algoritmos de aproximação entram em ação. Uma vez que esse custo for determinado, na segunda parte do trabalho, uma outra questão surge: ¿Como dividir esse custo entre todos os usuários que participam da rede de uma maneira ¿justa¿? Nesta parte, usaremos o compartilhamento de custos juntamente com as tecnicas de algoritmos de aproximação para responder a essa questão / Abstract: We consider the interplay of two areas: combinatorial optimization and cost-sharing in network design problems. In the first, we are interested to find a solution with small cost. In the second we would like to share the solution cost between its users. We present algorithms for the problems ¿Connected Facility Location¿ and ¿Rent-or-Buy¿. These two problems are NP-hard, since they have as a particular case the minimum Steiner tree problem, which is a known NP-hard problem. In the first part of this work, we have the following question as motivation: ¿how to design a good network, i.e., one that satisfies all problem requirements and minimize the overall network construction cost?¿ In this part, approximation algorithms takes action. Once this cost is determinated, in the second part of the work, another question arises: ¿How to distribute this cost among all users that participate in the network in a ¿fair¿ way? In this part, we will use cost-sharing together with approximation algorithms techniques to answer this question / Mestrado / Teoria da Computação / Mestre em Ciência da Computação

Real-time strategy games are an exciting area of research, as creating a game AI poses many challenges - from managing a single unit to completing an objective of the game. This thesis explores possible solutions to this task, using genetic programming and neuroevolution. It presents and compares findings and differences between the models. Both methods performed reasonably well, but genetic programming was found to be a bit more effective in performance and results.

This thesis deals with image classification based on genetic programming and coevolution. Genetic programming algorithms make generating executable structures possible, which allows us to design solutions in form of programs. Using coevolution with the fitness prediction lowers the amount of time consumed by fitness evaluation and, therefore, also the execution time. The thesis describes a theoretical background of evolutionary algorithms and, in particular, cartesian genetic programming. We also describe coevolutionary algorithms properties and especially the proposed method for the image classifier evolution using coevolution of fitness predictors, where the objective is to find a good compromise between the classification accuracy, design time and classifier complexity. A part of the thesis is implementation of the proposed method, conducting the experiments and comparison of obtained results with other methods.

The aim of this work is to design and implement a method for the evolutionary design of neural networks with generative encoding. The proposed method is based on J. F. Miller's approach and uses a brain model that is gradually developed and which allows extraction of traditional neural networks. The development of the brain is controlled by programs created using cartesian genetic programming. The project was implemented in Python with the use of Numpy library. Experiments have shown that the proposed method is able to construct neural networks that achieve over 90 % accuracy on smaller datasets. The method is also able to develop neural networks capable of solving multiple problems at once while slightly reducing accuracy.

Disertační práce je zaměřena na optimalizaci průběhu pracovních operací v logistických skladech a distribučních centrech. Hlavním cílem je optimalizovat procesy plánování, rozvrhování a odbavování. Jelikož jde o problém patřící do třídy složitosti NP-težký, je výpočetně velmi náročné nalézt optimální řešení. Motivací pro řešení této práce je vyplnění pomyslné mezery mezi metodami zkoumanými na vědecké a akademické půdě a metodami používanými v produkčních komerčních prostředích. Jádro optimalizačního algoritmu je založeno na základě genetického programování řízeného bezkontextovou gramatikou. Hlavním přínosem této práce je a) navrhnout nový optimalizační algoritmus, který respektuje následující optimalizační podmínky: celkový čas zpracování, využití zdrojů, a zahlcení skladových uliček, které může nastat během zpracování úkolů, b) analyzovat historická data z provozu skladu a vyvinout sadu testovacích příkladů, které mohou sloužit jako referenční výsledky pro další výzkum, a dále c) pokusit se předčit stanovené referenční výsledky dosažené kvalifikovaným a trénovaným operačním manažerem jednoho z největších skladů ve střední Evropě.

The goal of this Master's Thesis is finding a SAT solving heuristic by the application of an evolutionary algorithm. This thesis surveys various approaches used in SAT solving and some variants of evolutionary algorithms that are relevant to this topic. Afterwards the implementation of a linear genetic programming system that searches for a suitable heuristic for SAT problem instances is described, together with the implementation of a custom SAT solver which expoloits the output of the genetic program. Finally, the achieved results are summarized.