The modeling of trees is challenging due to their complex branching structures. Three different ways to generate trees are using real world data for reconstruction, interactive modeling methods and modeling with procedural or rule-based systems. Procedural content generation is the idea of using algorithms to automate content creation processes, and it is useful in plant modeling since it can generate a wide variety of plants that can adapt and react to the environment and changing conditions. This thesis focuses on and extends a procedural tree generation technique that uses a space colonization algorithm to model the tree branches' competition for space, and shifts the previous works' focus from realism to fantasy. The technique satisfied the idea of using interaction between the tree's internal and external factors to determine its final shape, by letting the designer control the where and the how of the tree's growth process. The implementation resulted in a tree generation application where the user's imagination decides the limit of what can be produced, and if that limit is reached can the application be used to randomly generate a wide variety of trees and tree-like structures. A motivation for many researchers in the procedural content generation area is how it can be used to augment human imagination. The result of this thesis can be used for that, by stepping away from the restrictions of realism, and with ease let the user generate widely diverse trees, that are not necessarily realistic but, in most cases, adapts to the idea of a tree.

Submitted by Maria Aparecida (cidazen@gmail.com) on 2017-04-07T11:44:34Z No. of bitstreams: 1 Marilia Marta.pdf: 1791339 bytes, checksum: 4b1d16d2c77f148ff69597765e114fa2 (MD5) / Made available in DSpace on 2017-04-07T11:44:34Z (GMT). No. of bitstreams: 1 Marilia Marta.pdf: 1791339 bytes, checksum: 4b1d16d2c77f148ff69597765e114fa2 (MD5) Previous issue date: 2017-02-20 / CAPES / Artificial intelligence (AI) is a branch of computer science that studies the intelligent behavior of living beings, and mimics this intelligence by deploying it in computer programs, machines and systems in order to solve problems related to searching, optimization, planning, control, automation, etc. One of the areas of artificial intelligence is evolutionary computation, which is inspired by the principle of natural evolution of species. Within the evolutionary computation several methods based on the intelligence of plants have been recently proposed. How the plants survive and adapt in harsh environments has aroused great interest of researchers in AI. It is remarkable that the life cycle of a plant is extremely intriguing. The way the plants reproduce, propagate, disperse their seeds and select the most resistant is undoubtedly an evidence of intelligence of plants when optimize their existence. In this sense, several computer algorithms based on the intelligent lifecycle of plants have been proposed recently, these algorithms are in many cases, simple to implement, and very efficient in solving complex problems. In this work, the performance of some algorithms, the flower pollination algorithm, strawberry plant algorithm, invasive weed optimization and plant life cycle algorithm, all of them based on the intelligent behavior of plants, are analyzed when applied to optimization of test functions, and they are also compared with classical genetic algorithms. / A inteligência artificial (IA) é um ramo da ciência da computação que estuda o comportamento inteligente dos seres vivos e imita essa inteligência implantando-a em programas de computador, máquinas e sistemas para resolver problemas relacionados à busca, otimização, planejamento, controle, automação, etc. Uma das áreas da inteligência artificial é a computação evolutiva, que é inspirada pelo princípio da evolução natural das espécies. Dentro da computação evolutiva vários métodos baseados na informação de plantas têm sido recentemente proposto. Como as plantas sobrevivem e se adaptam em ambientes agressivos tem despertado grande interesse dos pesquisadores em IA. O ciclo de vida de uma planta é extremamente intrigante. A maneira como as plantas se reproduzem, propagam, dispersam suas sementes e selecionam as mais resistentes é, sem dúvida, uma evidência de inteligência das plantas quando otimizam sua existência. Nesse sentido, diversos algoritmos computacionais baseados no ciclo de vida inteligente das plantas têm sido propostos nos anos recentes, esses algoritmos são, em muitos casos, simples de implementar e muito eficientes na solução de problemas complexos. Neste trabalho é analisado o desempenho de alguns desses algoritmos, o algoritmo de polinização de flores, o algoritmo de planta de morango, otimização invasiva de ervas daninhas e algoritmo do ciclo de vida da planta, todos baseados no comportamento inteligente das plantas, quando aplicados à otimização de funções teste e também comparados com algoritmos genéticos clássicos.