Please read the abstract in the section, 00front, of this document / Dissertation (MSc (Agric))--University of Pretoria, 2006. / Agricultural Economics, Extension and Rural Development / unrestricted

Please read the abstract in the section 00front of this document / Dissertation (MSc (Botany))--University of Pretoria, 2003. / Plant Science / unrestricted

Made available in DSpace on 2014-12-17T15:48:11Z (GMT). No. of bitstreams: 1 LigiaBB.pdf: 1036516 bytes, checksum: 36260a287f3ddf0bc38abbb0ec32b82f (MD5) Previous issue date: 2007-02-09 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / The Traveling Purchaser Problem is a variant of the Traveling Salesman Problem, where there is a set of markets and a set of products. Each product is available on a subset of markets and its unit cost depends on the market where it is available. The objective is to buy all the products, departing and returning to a domicile, at the least possible cost defined as the summation of the weights of the edges in the tour and the cost paid to acquire the products. A Transgenetic Algorithm, an evolutionary algorithm with basis on endosymbiosis, is applied to the Capacited and Uncapacited versions of this problem. Evolution in Transgenetic Algorithms is simulated with the interaction and information sharing between populations of individuals from distinct species. The computational results show that this is a very effective approach for the TPP regarding solution quality and runtime. Seventeen and nine new best results are presented for instances of the capacited and uncapacited versions, respectively / O Problema do Caixeiro Comprador ? uma variante do Problema do Caixeiro Viajante, onde existe um conjunto de mercados e um conjunto de produtos. Cada produto est? dispon?vel em um subconjunto de mercados e o pre?o da unidade varia de acordo com o mercado. O objetivo ? comprar todos os produtos, partindo e retornando para o dep?sito, de maneira que a soma do custo da rota e dos produtos seja m?nimo. Um Algoritmo Transgen?tico, algoritmo evolucion?rio com base na endosimbiose, ? utilizado para resolver a vers?o Capacitada e N?o Capacitada desse problema. A evolu??o no algoritmo transgen?tico ? simulada com a intera??o e troca de informa??es entre popula??o de indiv?duos de diferentes esp?cies. Os resultados computacionais mostram que a abordagem ? satisfat?ria para o PCC , tanto na qualidade da solu??o, quanto no tempo de execu??o. Dezessete e nove novas melhores solu??es s?o encontradas para o PCC Capacitado e para o PCC N?o Capacitado, respectivamente

Made available in DSpace on 2014-12-17T15:47:55Z (GMT). No. of bitstreams: 1 SilviaMDM_DISSERT.pdf: 1535044 bytes, checksum: 925f2f885f42335d55c35aa64bb4d026 (MD5) Previous issue date: 2011-02-17 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / The Multiobjective Spanning Tree is a NP-hard Combinatorial Optimization problem whose application arises in several areas, especially networks design. In this work, we propose a solution to the biobjective version of the problem through a Transgenetic Algorithm named ATIS-NP. The Computational Transgenetic is a metaheuristic technique from Evolutionary Computation whose inspiration relies in the conception of cooperation (and not competition) as the factor of main influence to evolution. The algorithm outlined is the evolution of a work that has already yielded two other transgenetic algorithms. In this sense, the algorithms previously developed are also presented. This research also comprises an experimental analysis with the aim of obtaining information related to the performance of ATIS-NP when compared to other approaches. Thus, ATIS-NP is compared to the algorithms previously implemented and to other transgenetic already presented for the problem under consideration. The computational experiments also address the comparison to two recent approaches from literature that present good results, a GRASP and a genetic algorithms. The efficiency of the method described is evaluated with basis in metrics of solution quality and computational time spent. Considering the problem is within the context of Multiobjective Optimization, quality indicators are adopted to infer the criteria of solution quality. Statistical tests evaluate the significance of results obtained from computational experiments / A ?rvore Geradora Multiobjetivo ? um problema de Otimiza??o Combinat?ria NP-?rduo. Esse problema possui aplica??o em diversas ?reas, em especial, no projeto de redes. Nesse trabalho, prop?e-se uma solu??o para o problema em sua vers?o biobjetivo por meio de um Algoritmo Transgen?tico, denominado ATIS-NP. A Transgen?tica Computacional ? uma t?cnica metaheur?stica da Computa??o Evolucion?ria cuja inspira??o est? na coopera??o (e n?o na competi??o) como fator de maior influ?ncia para a evolu??o. O algoritmo proposto ? a evolu??o de um trabalho que j? originou dois outros algoritmos transgen?ticos. Nesse sentido, os algoritmos previamente desenvolvidos tamb?m s?o apresentados. Essa pesquisa compreende ainda uma an?lise experimental que visa obter informa??es quanto ao desempenho do ATIS-NP quando comparado a outros algoritmos. Para tanto, o ATIS-NP ? comparado aos dois algoritmos anteriormente implementados, bem como a outro transgen?tico proposto na literatura para o problema tratado. Os experimentos computacionais abrangem ainda a compara??o do algoritmo desenvolvido a duas abordagens recentes da literatura que obt?m excelentes resultados, um GRASP e um gen?tico. A efici?ncia do m?todo apresentado ? avaliada com base em medidas de qualidade de solu??o e tempo computacional despendido. Uma vez que o problema se insere no contexto da Otimiza??o Multiobjetivo, indicadores de qualidade s?o utilizados para inferir o crit?rio de qualidade de solu??es obtidas. Testes estat?sticos avaliam a signific?ncia dos resultados obtidos nos experimentos computacionais

Made available in DSpace on 2014-12-17T14:55:03Z (GMT). No. of bitstreams: 1 IloneideCOR.pdf: 1010601 bytes, checksum: 76bbc04aa0a456f079121fb0d750ea74 (MD5) Previous issue date: 2005-03-03 / The problems of combinatory optimization have involved a large number of researchers in search of approximative solutions for them, since it is generally accepted that they are unsolvable in polynomial time. Initially, these solutions were focused on heuristics. Currently, metaheuristics are used more for this task, especially those based on evolutionary algorithms. The two main contributions of this work are: the creation of what is called an -Operon- heuristic, for the construction of the information chains necessary for the implementation of transgenetic (evolutionary) algorithms, mainly using statistical methodology - the Cluster Analysis and the Principal Component Analysis; and the utilization of statistical analyses that are adequate for the evaluation of the performance of the algorithms that are developed to solve these problems. The aim of the Operon is to construct good quality dynamic information chains to promote an -intelligent- search in the space of solutions. The Traveling Salesman Problem (TSP) is intended for applications based on a transgenetic algorithmic known as ProtoG. A strategy is also proposed for the renovation of part of the chromosome population indicated by adopting a minimum limit in the coefficient of variation of the adequation function of the individuals, with calculations based on the population. Statistical methodology is used for the evaluation of the performance of four algorithms, as follows: the proposed ProtoG, two memetic algorithms and a Simulated Annealing algorithm. Three performance analyses of these algorithms are proposed. The first is accomplished through the Logistic Regression, based on the probability of finding an optimal solution for a TSP instance by the algorithm being tested. The second is accomplished through Survival Analysis, based on a probability of the time observed for its execution until an optimal solution is achieved. The third is accomplished by means of a non-parametric Analysis of Variance, considering the Percent Error of the Solution (PES) obtained by the percentage in which the solution found exceeds the best solution available in the literature. Six experiments have been conducted applied to sixty-one instances of Euclidean TSP with sizes of up to 1,655 cities. The first two experiments deal with the adjustments of four parameters used in the ProtoG algorithm in an attempt to improve its performance. The last four have been undertaken to evaluate the performance of the ProtoG in comparison to the three algorithms adopted. For these sixty-one instances, it has been concluded on the grounds of statistical tests that there is evidence that the ProtoG performs better than these three algorithms in fifty instances. In addition, for the thirty-six instances considered in the last three trials in which the performance of the algorithms was evaluated through PES, it was observed that the PES average obtained with the ProtoG was less than 1% in almost half of these instances, having reached the greatest average for one instance of 1,173 cities, with an PES average equal to 3.52%. Therefore, the ProtoG can be considered a competitive algorithm for solving the TSP, since it is not rare in the literature find PESs averages greater than 10% to be reported for instances of this size. / Os problemas de otimiza??o combinat?ria t?m envolvido um grande n?mero de pesquisadores na busca por solu??es aproximativas para aqueles, desde a aceita??o de que eles s?o considerados insol?veis em tempo polinomial. Inicialmente, essas solu??es eram focalizadas por meio de heur?sticas. Atualmente, as metaheur?sticas s?o mais utilizadas para essa tarefa, especialmente aquelas baseadas em algoritmos evolucion?rios. As duas principais contribui??es deste trabalho s?o: a cria??o de uma heur?stica, denominada Operon, para a constru??o de cadeias de informa??es necess?rias ? implementa??o de algoritmos transgen?ticos (evolucion?rios) utilizando, principalmente, a metodologia estat?stica - An?lise de Agrupamentos e An?lise de Componentes Principais -; e a utiliza??o de an?lises estat?sticas adequadas ? avalia??o da performance de algoritmos destinados ? solu??o desses problemas. O Operon visa construir, de forma din?mica e de boa qualidade, cadeias de informa??es a fim de promover uma busca -inteligente- no espa?o de solu??es. O Problema do Caixeiro Viajante (PCV) ? focalizado para as aplica??es que s?o realizadas com base num algoritmo transgen?tico, denominado ProtoG. Prop?e-se, tamb?m, uma estrat?gia de renova??o de parte da popula??o de cromossomos indicada pela ado??o de um limite m?nimo no coeficiente de varia??o da fun??o de adequa??o dos indiv?duos, calculado com base na popula??o. S?o propostas tr?s an?lises estat?sticas para avaliar a performance de algoritmos. A primeira ? realizada atrav?s da An?lise de Regress?o Log?stica, com base na probabilidade de obten??o da solu??o ?tima de uma inst?ncia do PCV pelo algoritmo em teste. A segunda ? realizada atrav?s da An?lise de Sobreviv?ncia, com base numa probabilidade envolvendo o tempo de execu??o observado at? que a solu??o ?tima seja obtida. A terceira ? realizada por meio da An?lise de Vari?ncia n?o param?trica, considerando o Erro Percentual da Solu??o (EPS) obtido pela percentagem em que a solu??o encontrada excede a melhor solu??o dispon?vel na literatura. Utiliza-se essa metodologia para a avalia??o da performance de quatro algoritmos, a saber: o ProtoG proposto, dois algoritmos mem?ticos e um algoritmo Simulated Annealing. Foram realizados seis experimentos, aplicados a sessenta e uma inst?ncias do PCV euclidiano, com tamanhos de at? 1.655 cidades. Os dois primeiros experimentos tratam do ajuste de quatro par?metros utilizados no algoritmo ProtoG, visando melhorar a performance do mesmo. Os quatro ?ltimos s?o utilizados para avaliar a performance do ProtoG em compara??o aos tr?s algoritmos adotados. Para essas sessenta e uma inst?ncias, conclui-se, sob testes estat?sticos, que h? evid?ncias de que o ProtoG ? superior a esses tr?s algoritmos em cinq?enta inst?ncias. Al?m disso, para as trinta e seis inst?ncias consideradas nos tr?s ?ltimos experimentos, nos quais a avalia??o da performance dos algoritmos foi realizada com base no EPS, observou-se que o ProtoG obteve EPSs m?dios menores que 1% em quase metade das inst?ncias, tendo atingido a maior m?dia para uma inst?ncia composta por 1.173 cidades, com EPS m?dio igual a 3,52%. Logo, o ProtoG pode ser considerado um algoritmo competitivo para solucionar o PCV, pois n?o ? raro serem reportados, na literatura, EPSs m?dios maiores que 10% para inst?ncias desse porte.

Made available in DSpace on 2015-03-03T15:48:41Z (GMT). No. of bitstreams: 1 MatheusSM_TESE.pdf: 3657538 bytes, checksum: 05bf71663b044728a1e70b6db57b834e (MD5) Previous issue date: 2014-03-21 / This paper introduces a new variant of the Traveling Car Renter Problem, named Prizecollecting Traveling Car Renter Problem. In this problem, a set of vertices, each associated with a bonus, and a set of vehicles are given. The objective is to determine a cycle that visits some vertices collecting, at least, a pre-defined bonus, and minimizing the cost of the tour that can be traveled with different vehicles. A mathematical formulation is presented and implemented in a solver to produce results for sixty-two instances. The proposed problem is also subject of an experimental study based on the algorithmic application of four metaheuristics representing the best adaptations of the state of the art of the heuristic programming.We also provide new local search operators which exploit the neighborhoods of the problem, construction procedures and adjustments, created specifically for the addressed problem. Comparative computational experiments and performance tests are performed on a sample of 80 instances, aiming to offer a competitive algorithm to the problem. We conclude that memetic algorithms, computational transgenetic and a hybrid evolutive algorithm are competitive in tests performed / Este trabalho apresenta uma nova variante do problema do Caixeiro Alugador ainda n?o descrita na literatura, denominada de Caixeiro Alugador com Coleta de Pr?mios. Neste problema s?o disponibilizados um conjunto de v?rtices, cada um com um b?nus associado e um conjunto de ve?culos. O objetivo do problema ? determinar um ciclo que visite alguns v?rtices coletando, pelo menos, um b?nus pr?-de nido e minimizando os custos de viagem atrav?s da rota, que pode ser feita com ve?culos de diferentes tipos. ? apresentada uma formula??o matem?tica e implementada em um solver produzindo resultados em sessenta e duas inst?ncias. O problema proposto tamb?m ? objeto de um estudo algor?tmico experimental baseado na aplica??o de quatro metaheur?sticas de solu??o, representando adapta??es do melhor do estado da arte em programa??o heur?stica. Nesse trabalho tamb?m apresentamos a constitui??o de novos operadores que exploram as vizinhan?as do problema, procedimentos construtivos e adapta??es, criados especifi camente para o problema abordado. Experimentos computacionais comparativos e testes de desempenho s?o realizados sobre uma amostra de 80 inst?ncias, visando oferecer um algoritmo de solu??o competitivo para o problema. Conclui-se que algoritmos com abordagem mem?tica, transgen ?tica e evolucion?ria h?brida obtiveram resultados competitivos nos testes efetuados. Palavras-chave: Caixeiro Alugador com Coleta de Pr?mios. Metaheur?sticas. GRASP/VNS. Algoritmo Mem?tico. Transgen?tica Computacional. Computa??o Evolucion?ria

In early 2003, a persistent drought threatened about 15 million people in the Southern African region (SADC) with starvation as farmers in this region were not able to produce enough food. A similar threat was experienced in the United States of America (USA). The Americans responded by introducing GM technology, which thankfully stabilised corn production and food security. It was against this backdrop that the South African government legalised and supported GM technology in the farming industry. However, the technology became a contentious issue amongst scholars, politicians and policy makers as well as farmers. Therefore, this study analysed the perceptions of small-scale and large-scale farmers, located in Paarl, Western Cape, South Africa, on the adoption of GM technology. This qualitative study, using a case study design, collected primary data from thirty (30) farmers: fifteen (15) small-scale and fifteen (15) large-scale farmers. The findings revealed complex factors influencing farmers’ adoption decisions and that Adopter perception (AP) and Consumer perception (CP) play a key role in their adoption of GM technology. These commercially and profit-driven farmers avoid using GM technology because public opinion and the markets weigh heavily against it. It was concluded that the farmers regarded GM technology as just one of many agricultural technologies and not as an exception. It was also considered unaffordable and detrimental to the environment, the economy and their livelihoods.The study recommends that the government should fully investigate public perceptions with regard to the adoption of any new agricultural innovation prior to making policy decisions. / Development Studies / M.A. (Development Studies)